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1 logical one level
Микроэлектроника: логический уровень 1 -
2 logical one level
логічний рівень «1»English-Ukrainian dictionary of microelectronics > logical one level
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3 logical one level
English-Russian dictionary of microelectronics > logical one level
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4 level
1. ім.1) рівень2) ступінь (напр. інтеграції)2. дієсл. вирівнювати, розрівнювати - acceptor energy level
- algorithmic level
- allowed level
- automation level
- behavioral level
- chip complexity level
- chip level
- circuit level
- circuit complexity level
- complexity level
- concentration level
- confidence level
- damage level
- dc level
- deep level
- defect level
- degenerate level
- discrete energy level
- discrete level
- donor energy level
- doping level
- dynamic level
- electrical level
- electron quasi-Fermi level
- empty level
- filled level
- functional level
- functionality level
- gate level
- hole quasi-Fermi level
- impurity level
- input level
- integration level
- interconnection level
- logic level
- logical one level
- logical zero level
- logic gate level
- mask level
- masking level
- metallization level
- noise level
- occupied level
- register transfer level
- resistivity level
- saturation level
- shallow level
- sheet-resistance level
- steady-state level
- submicron level
- superficial level
- switch level
- transistor switch level
- trapping level
- TTL level
- two-resist level
- unfilled level
- unknown logic level
- vacant level
- wafer level -
5 level
1) уровень2) степень•- activity level
- addressing level
- alarm level
- application level
- arousal level
- at chip level
- authority level
- bit level
- cluster level
- conceptual level
- confidence level
- data level
- device level
- direct current level
- discrete level
- ECL level
- error level
- function level
- indeterminate level
- input level
- integration level
- intensity level
- interrupt level
- kernel execution level
- large scale integration level
- level of abstraction
- level of confidence
- level of mapping
- level of web-presence
- linguistic level
- links redundancy level
- logic-0 level
- logic-1 level
- logical level
- LSI level
- medium scale integration level
- MSI level
- nesting level
- network level
- noise level
- one level
- operating level
- overload level
- presentation level
- priority level
- pumping level
- pump level
- quantizing level
- receiver level
- reference level
- regional level
- relative-transmission level
- safety-integrity level
- session level
- significance level
- staircase level
- static level
- support level
- system level
- threshold level
- traffic level
- transmission level
- transport level
- trigger level
- user level
- very large-scale integration level
- VLSI level
- zero levelEnglish-Russian dictionary of computer science and programming > level
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6 logical address
address bus — шина адреса; адресная шина
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7 logical high
логическая единица
сигнал логической единицы
-
[Интент]
сигнал логической единицы
—
[Л.Г.Суменко. Англо-русский словарь по информационным технологиям. М.: ГП ЦНИИС, 2003.]Параллельные тексты EN-RU
This is only true when only one binary signal input is set to a logic level of "1".
[Schneider Electric]Это условие выполняется только в том случае, если сигнал логической единицы присутствует только на одном двоичном входе.
[Перевод Интент]
Тематики
- Булева алгебра, элементы цифровой техники
Синонимы
EN
- log. high
- logic level of "1"
- logic value of "1"
- logical high
- mark
Англо-русский словарь нормативно-технической терминологии > logical high
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8 bay level functions
функции уровня присоединения в системе автоматизации подстанции
Функции системы управления подстанцией, которые используют данные одного присоединения и которые выполняются на основном оборудовании этого присоединения, связываясь через логический интерфейс 3 на уровне присоединения и через логические интерфейсы 4 и 5 с уровнем процесса.
Примечание. На рисунке ДА.1 (см. приложение ДА) приведены модель интерфейсов в системе автоматизации подстанции и условные номера интерфейсов.
[ ГОСТ Р 54325-2011 (IEC/TS 61850-2:2003)]EN
bay level functions
functions that use mainly the data of one bay and act mainly on the primary equipment of that bay. Bay level functions communicate via logical interface 3 within the bay level and via the logical interfaces 4 and 5 to the process level, i.e. with any kind of remote input/output or with intelligent sensors and actuators
Examples Feeder or transformer, protection, control and interlocking.
[IEC 61850-2, ed. 1.0 (2003-08)]Тематики
EN
Англо-русский словарь нормативно-технической терминологии > bay level functions
-
9 process related station level functions
- технологические функции станционного уровня системы автоматизации подстанции
технологические функции станционного уровня системы автоматизации подстанции
Функции, использующие данные более чем одного присоединения или всей подстанции и воздействующие на первичное оборудование более чем одного присоединения или на оборудование всей подстанции.
Примечание. Эти функции сообщаются в основном через логический интерфейс 8.
[ ГОСТ Р 54325-2011 (IEC/TS 61850-2:2003)]EN
process related station level functions
use data from more than one bay, or from the whole substation and act on the primary equipment of more than one bay, or on the primary equipment of the whole substation. Examples of such functions are: station wide interlocking, automatic sequencers, and busbar protection. These functions communicate mainly via the logical interface 8
[IEC 61850-2, ed. 1.0 (2003-08)]Тематики
EN
Англо-русский словарь нормативно-технической терминологии > process related station level functions
-
10 logic level of "1"
логическая единица
сигнал логической единицы
-
[Интент]
сигнал логической единицы
—
[Л.Г.Суменко. Англо-русский словарь по информационным технологиям. М.: ГП ЦНИИС, 2003.]Параллельные тексты EN-RU
This is only true when only one binary signal input is set to a logic level of "1".
[Schneider Electric]Это условие выполняется только в том случае, если сигнал логической единицы присутствует только на одном двоичном входе.
[Перевод Интент]
Тематики
- Булева алгебра, элементы цифровой техники
Синонимы
EN
- log. high
- logic level of "1"
- logic value of "1"
- logical high
- mark
Англо-русский словарь нормативно-технической терминологии > logic level of "1"
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11 Language
Philosophy is written in that great book, the universe, which is always open, right before our eyes. But one cannot understand this book without first learning to understand the language and to know the characters in which it is written. It is written in the language of mathematics, and the characters are triangles, circles, and other figures. Without these, one cannot understand a single word of it, and just wanders in a dark labyrinth. (Galileo, 1990, p. 232)It never happens that it [a nonhuman animal] arranges its speech in various ways in order to reply appropriately to everything that may be said in its presence, as even the lowest type of man can do. (Descartes, 1970a, p. 116)It is a very remarkable fact that there are none so depraved and stupid, without even excepting idiots, that they cannot arrange different words together, forming of them a statement by which they make known their thoughts; while, on the other hand, there is no other animal, however perfect and fortunately circumstanced it may be, which can do the same. (Descartes, 1967, p. 116)Human beings do not live in the object world alone, nor alone in the world of social activity as ordinarily understood, but are very much at the mercy of the particular language which has become the medium of expression for their society. It is quite an illusion to imagine that one adjusts to reality essentially without the use of language and that language is merely an incidental means of solving specific problems of communication or reflection. The fact of the matter is that the "real world" is to a large extent unconsciously built on the language habits of the group.... We see and hear and otherwise experience very largely as we do because the language habits of our community predispose certain choices of interpretation. (Sapir, 1921, p. 75)It powerfully conditions all our thinking about social problems and processes.... No two languages are ever sufficiently similar to be considered as representing the same social reality. The worlds in which different societies live are distinct worlds, not merely the same worlds with different labels attached. (Sapir, 1985, p. 162)[A list of language games, not meant to be exhaustive:]Giving orders, and obeying them- Describing the appearance of an object, or giving its measurements- Constructing an object from a description (a drawing)Reporting an eventSpeculating about an eventForming and testing a hypothesisPresenting the results of an experiment in tables and diagramsMaking up a story; and reading itPlay actingSinging catchesGuessing riddlesMaking a joke; and telling itSolving a problem in practical arithmeticTranslating from one language into anotherLANGUAGE Asking, thanking, cursing, greeting, and praying-. (Wittgenstein, 1953, Pt. I, No. 23, pp. 11 e-12 e)We dissect nature along lines laid down by our native languages.... The world is presented in a kaleidoscopic flux of impressions which has to be organized by our minds-and this means largely by the linguistic systems in our minds.... No individual is free to describe nature with absolute impartiality but is constrained to certain modes of interpretation even while he thinks himself most free. (Whorf, 1956, pp. 153, 213-214)We dissect nature along the lines laid down by our native languages.The categories and types that we isolate from the world of phenomena we do not find there because they stare every observer in the face; on the contrary, the world is presented in a kaleidoscopic flux of impressions which has to be organized by our minds-and this means largely by the linguistic systems in our minds.... We are thus introduced to a new principle of relativity, which holds that all observers are not led by the same physical evidence to the same picture of the universe, unless their linguistic backgrounds are similar or can in some way be calibrated. (Whorf, 1956, pp. 213-214)9) The Forms of a Person's Thoughts Are Controlled by Unperceived Patterns of His Own LanguageThe forms of a person's thoughts are controlled by inexorable laws of pattern of which he is unconscious. These patterns are the unperceived intricate systematizations of his own language-shown readily enough by a candid comparison and contrast with other languages, especially those of a different linguistic family. (Whorf, 1956, p. 252)It has come to be commonly held that many utterances which look like statements are either not intended at all, or only intended in part, to record or impart straightforward information about the facts.... Many traditional philosophical perplexities have arisen through a mistake-the mistake of taking as straightforward statements of fact utterances which are either (in interesting non-grammatical ways) nonsensical or else intended as something quite different. (Austin, 1962, pp. 2-3)In general, one might define a complex of semantic components connected by logical constants as a concept. The dictionary of a language is then a system of concepts in which a phonological form and certain syntactic and morphological characteristics are assigned to each concept. This system of concepts is structured by several types of relations. It is supplemented, furthermore, by redundancy or implicational rules..., representing general properties of the whole system of concepts.... At least a relevant part of these general rules is not bound to particular languages, but represents presumably universal structures of natural languages. They are not learned, but are rather a part of the human ability to acquire an arbitrary natural language. (Bierwisch, 1970, pp. 171-172)In studying the evolution of mind, we cannot guess to what extent there are physically possible alternatives to, say, transformational generative grammar, for an organism meeting certain other physical conditions characteristic of humans. Conceivably, there are none-or very few-in which case talk about evolution of the language capacity is beside the point. (Chomsky, 1972, p. 98)[It is] truth value rather than syntactic well-formedness that chiefly governs explicit verbal reinforcement by parents-which renders mildly paradoxical the fact that the usual product of such a training schedule is an adult whose speech is highly grammatical but not notably truthful. (R. O. Brown, 1973, p. 330)he conceptual base is responsible for formally representing the concepts underlying an utterance.... A given word in a language may or may not have one or more concepts underlying it.... On the sentential level, the utterances of a given language are encoded within a syntactic structure of that language. The basic construction of the sentential level is the sentence.The next highest level... is the conceptual level. We call the basic construction of this level the conceptualization. A conceptualization consists of concepts and certain relations among those concepts. We can consider that both levels exist at the same point in time and that for any unit on one level, some corresponding realizate exists on the other level. This realizate may be null or extremely complex.... Conceptualizations may relate to other conceptualizations by nesting or other specified relationships. (Schank, 1973, pp. 191-192)The mathematics of multi-dimensional interactive spaces and lattices, the projection of "computer behavior" on to possible models of cerebral functions, the theoretical and mechanical investigation of artificial intelligence, are producing a stream of sophisticated, often suggestive ideas.But it is, I believe, fair to say that nothing put forward until now in either theoretic design or mechanical mimicry comes even remotely in reach of the most rudimentary linguistic realities. (Steiner, 1975, p. 284)The step from the simple tool to the master tool, a tool to make tools (what we would now call a machine tool), seems to me indeed to parallel the final step to human language, which I call reconstitution. It expresses in a practical and social context the same understanding of hierarchy, and shows the same analysis by function as a basis for synthesis. (Bronowski, 1977, pp. 127-128)t is the language donn eґ in which we conduct our lives.... We have no other. And the danger is that formal linguistic models, in their loosely argued analogy with the axiomatic structure of the mathematical sciences, may block perception.... It is quite conceivable that, in language, continuous induction from simple, elemental units to more complex, realistic forms is not justified. The extent and formal "undecidability" of context-and every linguistic particle above the level of the phoneme is context-bound-may make it impossible, except in the most abstract, meta-linguistic sense, to pass from "pro-verbs," "kernals," or "deep deep structures" to actual speech. (Steiner, 1975, pp. 111-113)A higher-level formal language is an abstract machine. (Weizenbaum, 1976, p. 113)Jakobson sees metaphor and metonymy as the characteristic modes of binarily opposed polarities which between them underpin the two-fold process of selection and combination by which linguistic signs are formed.... Thus messages are constructed, as Saussure said, by a combination of a "horizontal" movement, which combines words together, and a "vertical" movement, which selects the particular words from the available inventory or "inner storehouse" of the language. The combinative (or syntagmatic) process manifests itself in contiguity (one word being placed next to another) and its mode is metonymic. The selective (or associative) process manifests itself in similarity (one word or concept being "like" another) and its mode is metaphoric. The "opposition" of metaphor and metonymy therefore may be said to represent in effect the essence of the total opposition between the synchronic mode of language (its immediate, coexistent, "vertical" relationships) and its diachronic mode (its sequential, successive, lineal progressive relationships). (Hawkes, 1977, pp. 77-78)It is striking that the layered structure that man has given to language constantly reappears in his analyses of nature. (Bronowski, 1977, p. 121)First, [an ideal intertheoretic reduction] provides us with a set of rules"correspondence rules" or "bridge laws," as the standard vernacular has it-which effect a mapping of the terms of the old theory (T o) onto a subset of the expressions of the new or reducing theory (T n). These rules guide the application of those selected expressions of T n in the following way: we are free to make singular applications of their correspondencerule doppelgangers in T o....Second, and equally important, a successful reduction ideally has the outcome that, under the term mapping effected by the correspondence rules, the central principles of T o (those of semantic and systematic importance) are mapped onto general sentences of T n that are theorems of Tn. (P. Churchland, 1979, p. 81)If non-linguistic factors must be included in grammar: beliefs, attitudes, etc. [this would] amount to a rejection of the initial idealization of language as an object of study. A priori such a move cannot be ruled out, but it must be empirically motivated. If it proves to be correct, I would conclude that language is a chaos that is not worth studying.... Note that the question is not whether beliefs or attitudes, and so on, play a role in linguistic behavior and linguistic judgments... [but rather] whether distinct cognitive structures can be identified, which interact in the real use of language and linguistic judgments, the grammatical system being one of these. (Chomsky, 1979, pp. 140, 152-153)23) Language Is Inevitably Influenced by Specific Contexts of Human InteractionLanguage cannot be studied in isolation from the investigation of "rationality." It cannot afford to neglect our everyday assumptions concerning the total behavior of a reasonable person.... An integrational linguistics must recognize that human beings inhabit a communicational space which is not neatly compartmentalized into language and nonlanguage.... It renounces in advance the possibility of setting up systems of forms and meanings which will "account for" a central core of linguistic behavior irrespective of the situation and communicational purposes involved. (Harris, 1981, p. 165)By innate [linguistic knowledge], Chomsky simply means "genetically programmed." He does not literally think that children are born with language in their heads ready to be spoken. He merely claims that a "blueprint is there, which is brought into use when the child reaches a certain point in her general development. With the help of this blueprint, she analyzes the language she hears around her more readily than she would if she were totally unprepared for the strange gabbling sounds which emerge from human mouths. (Aitchison, 1987, p. 31)Looking at ourselves from the computer viewpoint, we cannot avoid seeing that natural language is our most important "programming language." This means that a vast portion of our knowledge and activity is, for us, best communicated and understood in our natural language.... One could say that natural language was our first great original artifact and, since, as we increasingly realize, languages are machines, so natural language, with our brains to run it, was our primal invention of the universal computer. One could say this except for the sneaking suspicion that language isn't something we invented but something we became, not something we constructed but something in which we created, and recreated, ourselves. (Leiber, 1991, p. 8)Historical dictionary of quotations in cognitive science > Language
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12 address
адрес || адресовать-
absolute address
-
access address
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actual address
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base address
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blank address
-
call address
-
deferred address
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direct address
-
dummy address
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effective address
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executive address
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explicit address
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first-level address
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home address
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immediate address
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implicit address
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implied address
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indexed address
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indirect address
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instruction address
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logical address
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logic address
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machine address
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magazine address
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memory address
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one-level address
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operand address
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page address
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physical address
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pointer address
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program address
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real address
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reference address
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relative address
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relocatable address
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result address
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return address
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second-level address
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segment-relative address
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single-level address
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source address
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specific address
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starting address
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start address
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stop address
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storage address
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symbolic address
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third-level address
- time code address -
time address
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track address
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true address
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two-level address
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variable address
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virtual address
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zero address
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zero-level address -
13 address
адрес || адресовать- absolute address
- actual address
- address of address
- allophone address
- arithmetic address
- auxiliary address
- B address
- base address
- binary-coded address
- blank address
- block address
- broadcast address
- broken address
- calculated address
- call address
- constant address
- coordinate address
- core memory address
- current address
- data address
- destination address
- direct address
- dot address
- drop address
- dummy address
- effective address
- e-mail address
- end address
- entry-point address
- executive address
- explicit address
- external device address
- external address
- extra address
- final address
- first-level address
- fixed address
- floating address
- floating-point address
- foreign address
- frame address
- generated address
- global address
- hash address
- high load address
- higher address
- home address
- host address
- host apparent address
- immediate address
- implicit address
- indexed address
- indexing address
- indirect address
- initial address
- instruction address
- interleaved addresses
- invalid address - key address
- last field address
- leading address
- link address
- linkage address
- listener address
- load-point address
- load address
- location address
- logical address
- lower address - memory address
- multicast address
- multilevel address
- native address
- network address
- Nth-level address
- number address
- octal address
- offset address
- one-level address
- operand address
- out-of-range address
- overflow exit address
- page address
- physical address
- pointer address
- preset address
- presumptive address
- program address
- real address
- reference address
- regional address
- relative address
- relocatable address
- relocation address
- restart address
- result address
- return address
- second-level address
- self-relative address
- sense address
- single-level address
- source address
- specific address
- starting address
- start address
- stop address
- storage address
- store address
- subnet address
- subroutine return address
- symbolic address
- synthetic address
- talker address
- talk address
- transport address
- true address
- two-coordinate address
- two-level address
- unique address
- unload address
- variable address
- vector address
- virtual address
- windowed address
- word address
- zero address
- zero-level addressEnglish-Russian dictionary of computer science and programming > address
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14 memory
1) памятьа) вчт запоминающее устройство, ЗУб) вчт совокупность физических и ( или) эмулируемых элементов, используемых в качестве запоминающего устройства2) запоминание3) фтт память формы•- annex memory
- antishock memory
- arm-position memory
- associative memory
- aural memory
- auxiliary memory
- available memory
- available user memory
- back-up memory
- base memory - bit-mapped memory
- bit-oriented memory
- boot flash memory
- bootstrap memory
- bubble memory
- bubble-lattice memory
- buffer memory
- bulk memory - cache memory
- cached memory - cassette memory
- charge-coupled device memory
- charge-transfer device memory
- CMOS memory
- command-chained memory - concurrent Rambus dynamic random access-memory
- content-addressable memory
- continuously charge-coupled random-access-memory - core memory
- counter memory
- cross-tie memory - cylindrical-domain memory
- data flash memory
- declarative memory
- demand-paged virtual memory
- destructive-readout memory - domain memory
- domain-tip memory
- domain-type propagation memory - dual-ported video memory
- dynamic memory - emotional memory - episodic memory
- erasable memory - error detection and correction memory
- expanded memory
- explicit memory - eye memory
- factory-programmable read-only memory
- fast memory - ferrite-sheet memory - file memory
- fixed memory
- flash memory
- flashbulb memory
- fluorescent disk read-only memory
- free memory
- fusible-link programmable read-only memory
- fuzzy associative memory
- genetic memory
- giant-magnetoresistance random-access memory - immediate memory
- immediate access memory
- implicit memory
- installed memory
- internal memory
- intrinsic memory
- involuntary memory
- Josephson memory - logical memory
- long-term memory
- low-temperature memory - main memory
- mask-programmable read-only memory
- matrix-readout memory
- mechanical memory
- mercury memory - motor memory - nonvolatile memory - on-chip memory
- one-level memory - paged memory
- paging memory - permanently allocated memory - photochromic memory
- physical memory
- piggyback-twistor semipermanent memory
- planar bubble memory
- plated-wire memory
- Pockels readout optical memory
- primary memory
- procedural memory - programmable memory
- programmable read-only memory
- prolonged memory
- protein memory
- push-down memory - refresh memory
- repertory memory - reserved memory
- rotating memory - search memory
- segmented bubble memory
- sensory memory
- sequential memory
- sequential access memory
- shadow memory
- shadow random access memory
- shadow read-only memory
- shallow memory
- shared memory
- short-term memory
- single-ported video memory
- slow memory - synchronous active memory - total memory
- total memory under 1 MB
- tse flip-flop memory
- twin-bank memory
- ultra-violet erasable programmable read-only-memory - verbal memory
- vertical Bloch-line memory
- video memory
- video disk memory - volatile memory
- wagon memory - working memory -
15 memory
1) памятьа) вчт. запоминающее устройство, ЗУб) вчт. совокупность физических и или эмулируемых элементов, используемых в качестве запоминающего устройства2) запоминание3) фтт. память формы•- adaptive bidirectional associative memory
- alterable memory
- annex memory
- antishock memory
- arm-position memory
- associative memory
- aural memory
- auxiliary memory
- available memory
- available user memory
- back-up memory
- base memory
- bidirectional associative memory
- biopolymer memory
- bipolar read-only memory
- bipolar-transistor memory
- bit-mapped memory
- bit-oriented memory
- boot flash memory
- bootstrap memory
- bubble memory
- bubble-lattice memory
- buffer memory
- bulk memory
- burst extended data output dynamic random-access memory
- byte addressable memory
- cache memory
- cached dynamic random access memory
- cached memory
- cached video random access memory
- card memory
- cassette memory
- charge-coupled device memory
- charge-transfer device memory
- CMOS memory
- command-chained memory
- compact disk read-only memory extended architecture mode 1
- compact disk read-only memory extended architecture mode 2
- compact disk read-only memory extended architecture
- compact disk read-only memory
- conception memory
- concurrent Rambus dynamic random access memory
- content-addressable memory
- continuously charge-coupled random-access memory
- control read-only memory
- conventional memory
- core memory
- counter memory
- cross-tie memory
- cryogenic continuous film memory
- current-access magnetic bubble memory
- cylindrical-domain memory
- data flash memory
- declarative memory
- demand-paged virtual memory
- destructive-readout memory
- digital versatile disk random access memory
- digital versatile disk read-only memory
- direct memory
- direct Rambus dynamic random access memory
- discrete bidirectional associative memory
- disk memory
- domain memory
- domain-tip memory
- domain-type propagation memory
- double data rate synchronous dynamic random access memory
- DRO memory
- dual-ported video memory
- dynamic memory
- dynamic random access memory
- EDAC memory
- electrically alterable read-only memory
- electrically erasable programmable read-only memory
- electrically erasable read-only memory
- electron-beam memory
- electron-beam-accessed memory
- electronically addressable memory
- emotional memory
- enhanced dynamic random access memory
- enhanced synchronous dynamic random access memory memory
- episodic memory
- erasable memory
- erasable programmable read-only memory
- error correcting memory
- error detection and correction memory
- expanded memory
- explicit memory
- extended architecture ready compact disk read-only memory
- extended conventional memory
- extended data output dynamic random access memory
- extended data output video random access memory
- extended memory
- external memory
- eye memory
- factory-programmable read-only memory
- fast memory
- fast page mode dynamic random-access memory
- ferric random-access memory
- ferrite-core memory
- ferrite-sheet memory
- ferroelectric random access memory
- field-programmable read-only memory
- file memory
- fixed memory
- flash memory
- flashbulb memory
- fluorescent disk read-only memory
- free memory
- fusible-link programmable read-only memory
- fuzzy associative memory
- genetic memory
- giant-magnetoresistance random-access memory
- high memory
- image memory
- immediate access memory
- immediate memory
- implicit memory
- installed memory
- internal memory
- intrinsic memory
- involuntary memory
- Josephson memory
- keyed-access erasable programmable read-only memory
- line-addressable random-access memory
- linear associative memory
- local memory
- logical memory
- long-term memory
- low-temperature memory
- magnetic random access memory
- magnetic thin-film memory
- magnetic tunnel junction random-access memory
- magnetoelectronic memory
- main memory
- mask-programmable read-only memory
- matrix-readout memory
- mechanical memory
- mercury memory
- metal-oxide-semiconductor electrically-alterable read-only memory
- microprogram memory
- motor memory
- multibank dynamic random access memory
- N-level memory
- nonvolatile memory
- nonvolatile random-access memory
- off-chip memory
- on-chip memory
- one-level memory
- optimal linear associative memory
- ovonic memory
- paged memory
- paging memory
- parameter random-access memory
- permanent memory
- permanently allocated memory
- personality electrically erasable programmable read-only memory
- personality erasable programmable read-only memory
- photochromic memory
- physical memory
- piggyback-twistor semipermanent memory
- planar bubble memory
- plated-wire memory
- Pockels readout optical memory
- primary memory
- procedural memory
- processor information read-only memory
- program flash memory
- programmable memory
- programmable read-only memory
- prolonged memory
- protein memory
- push-down memory
- Rambus dynamic random access memory
- random access memory
- read/write memory
- read-only memory
- refresh memory
- repertory memory
- reprogrammable read-only memory
- reserve memory
- reserved memory
- rotating memory
- scratch-pad memory
- screen memory
- search memory
- segmented bubble memory
- sensory memory
- sequential access memory
- sequential memory
- shadow memory
- shadow random access memory
- shadow read-only memory
- shallow memory
- shared memory
- short-term memory
- single-ported video memory
- slow memory
- sparse distributed associative memory
- stack memory
- standard dynamic random-access memory
- static memory
- static random access memory
- superhigh-speed memory
- synchronous active memory
- synchronous dynamic random access memory
- synchronous graphics random access memory
- synchronous video random access memory
- system management random access memory
- system memory
- temporal associative memory
- total memory under 1 MB
- total memory
- tse flip-flop memory
- twin-bank memory
- ultra-violet erasable programmable read-only memory
- upper memory
- used memory
- verbal memory
- vertical Bloch-line memory
- video disk memory
- video memory
- video random access memory
- virtual channel memory synchronous dynamic random access memory
- virtual memory
- visual memory
- volatile memory
- wagon memory
- window random access memory
- word-organized memory
- working memory
- write-only memoryThe New English-Russian Dictionary of Radio-electronics > memory
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16 tree
1) дерево, древо || засаживать деревьями2) древовидная схема; древовидная структура3) т. граф. дерево4) pl дендритные образования на катоде ( при электроосаждении металлов)- mixed star tree - plane tree- set tree -
17 Artificial Intelligence
In my opinion, none of [these programs] does even remote justice to the complexity of human mental processes. Unlike men, "artificially intelligent" programs tend to be single minded, undistractable, and unemotional. (Neisser, 1967, p. 9)Future progress in [artificial intelligence] will depend on the development of both practical and theoretical knowledge.... As regards theoretical knowledge, some have sought a unified theory of artificial intelligence. My view is that artificial intelligence is (or soon will be) an engineering discipline since its primary goal is to build things. (Nilsson, 1971, pp. vii-viii)Most workers in AI [artificial intelligence] research and in related fields confess to a pronounced feeling of disappointment in what has been achieved in the last 25 years. Workers entered the field around 1950, and even around 1960, with high hopes that are very far from being realized in 1972. In no part of the field have the discoveries made so far produced the major impact that was then promised.... In the meantime, claims and predictions regarding the potential results of AI research had been publicized which went even farther than the expectations of the majority of workers in the field, whose embarrassments have been added to by the lamentable failure of such inflated predictions....When able and respected scientists write in letters to the present author that AI, the major goal of computing science, represents "another step in the general process of evolution"; that possibilities in the 1980s include an all-purpose intelligence on a human-scale knowledge base; that awe-inspiring possibilities suggest themselves based on machine intelligence exceeding human intelligence by the year 2000 [one has the right to be skeptical]. (Lighthill, 1972, p. 17)4) Just as Astronomy Succeeded Astrology, the Discovery of Intellectual Processes in Machines Should Lead to a Science, EventuallyJust as astronomy succeeded astrology, following Kepler's discovery of planetary regularities, the discoveries of these many principles in empirical explorations on intellectual processes in machines should lead to a science, eventually. (Minsky & Papert, 1973, p. 11)5) Problems in Machine Intelligence Arise Because Things Obvious to Any Person Are Not Represented in the ProgramMany problems arise in experiments on machine intelligence because things obvious to any person are not represented in any program. One can pull with a string, but one cannot push with one.... Simple facts like these caused serious problems when Charniak attempted to extend Bobrow's "Student" program to more realistic applications, and they have not been faced up to until now. (Minsky & Papert, 1973, p. 77)What do we mean by [a symbolic] "description"? We do not mean to suggest that our descriptions must be made of strings of ordinary language words (although they might be). The simplest kind of description is a structure in which some features of a situation are represented by single ("primitive") symbols, and relations between those features are represented by other symbols-or by other features of the way the description is put together. (Minsky & Papert, 1973, p. 11)[AI is] the use of computer programs and programming techniques to cast light on the principles of intelligence in general and human thought in particular. (Boden, 1977, p. 5)The word you look for and hardly ever see in the early AI literature is the word knowledge. They didn't believe you have to know anything, you could always rework it all.... In fact 1967 is the turning point in my mind when there was enough feeling that the old ideas of general principles had to go.... I came up with an argument for what I called the primacy of expertise, and at the time I called the other guys the generalists. (Moses, quoted in McCorduck, 1979, pp. 228-229)9) Artificial Intelligence Is Psychology in a Particularly Pure and Abstract FormThe basic idea of cognitive science is that intelligent beings are semantic engines-in other words, automatic formal systems with interpretations under which they consistently make sense. We can now see why this includes psychology and artificial intelligence on a more or less equal footing: people and intelligent computers (if and when there are any) turn out to be merely different manifestations of the same underlying phenomenon. Moreover, with universal hardware, any semantic engine can in principle be formally imitated by a computer if only the right program can be found. And that will guarantee semantic imitation as well, since (given the appropriate formal behavior) the semantics is "taking care of itself" anyway. Thus we also see why, from this perspective, artificial intelligence can be regarded as psychology in a particularly pure and abstract form. The same fundamental structures are under investigation, but in AI, all the relevant parameters are under direct experimental control (in the programming), without any messy physiology or ethics to get in the way. (Haugeland, 1981b, p. 31)There are many different kinds of reasoning one might imagine:Formal reasoning involves the syntactic manipulation of data structures to deduce new ones following prespecified rules of inference. Mathematical logic is the archetypical formal representation. Procedural reasoning uses simulation to answer questions and solve problems. When we use a program to answer What is the sum of 3 and 4? it uses, or "runs," a procedural model of arithmetic. Reasoning by analogy seems to be a very natural mode of thought for humans but, so far, difficult to accomplish in AI programs. The idea is that when you ask the question Can robins fly? the system might reason that "robins are like sparrows, and I know that sparrows can fly, so robins probably can fly."Generalization and abstraction are also natural reasoning process for humans that are difficult to pin down well enough to implement in a program. If one knows that Robins have wings, that Sparrows have wings, and that Blue jays have wings, eventually one will believe that All birds have wings. This capability may be at the core of most human learning, but it has not yet become a useful technique in AI.... Meta- level reasoning is demonstrated by the way one answers the question What is Paul Newman's telephone number? You might reason that "if I knew Paul Newman's number, I would know that I knew it, because it is a notable fact." This involves using "knowledge about what you know," in particular, about the extent of your knowledge and about the importance of certain facts. Recent research in psychology and AI indicates that meta-level reasoning may play a central role in human cognitive processing. (Barr & Feigenbaum, 1981, pp. 146-147)Suffice it to say that programs already exist that can do things-or, at the very least, appear to be beginning to do things-which ill-informed critics have asserted a priori to be impossible. Examples include: perceiving in a holistic as opposed to an atomistic way; using language creatively; translating sensibly from one language to another by way of a language-neutral semantic representation; planning acts in a broad and sketchy fashion, the details being decided only in execution; distinguishing between different species of emotional reaction according to the psychological context of the subject. (Boden, 1981, p. 33)Can the synthesis of Man and Machine ever be stable, or will the purely organic component become such a hindrance that it has to be discarded? If this eventually happens-and I have... good reasons for thinking that it must-we have nothing to regret and certainly nothing to fear. (Clarke, 1984, p. 243)The thesis of GOFAI... is not that the processes underlying intelligence can be described symbolically... but that they are symbolic. (Haugeland, 1985, p. 113)14) Artificial Intelligence Provides a Useful Approach to Psychological and Psychiatric Theory FormationIt is all very well formulating psychological and psychiatric theories verbally but, when using natural language (even technical jargon), it is difficult to recognise when a theory is complete; oversights are all too easily made, gaps too readily left. This is a point which is generally recognised to be true and it is for precisely this reason that the behavioural sciences attempt to follow the natural sciences in using "classical" mathematics as a more rigorous descriptive language. However, it is an unfortunate fact that, with a few notable exceptions, there has been a marked lack of success in this application. It is my belief that a different approach-a different mathematics-is needed, and that AI provides just this approach. (Hand, quoted in Hand, 1985, pp. 6-7)We might distinguish among four kinds of AI.Research of this kind involves building and programming computers to perform tasks which, to paraphrase Marvin Minsky, would require intelligence if they were done by us. Researchers in nonpsychological AI make no claims whatsoever about the psychological realism of their programs or the devices they build, that is, about whether or not computers perform tasks as humans do.Research here is guided by the view that the computer is a useful tool in the study of mind. In particular, we can write computer programs or build devices that simulate alleged psychological processes in humans and then test our predictions about how the alleged processes work. We can weave these programs and devices together with other programs and devices that simulate different alleged mental processes and thereby test the degree to which the AI system as a whole simulates human mentality. According to weak psychological AI, working with computer models is a way of refining and testing hypotheses about processes that are allegedly realized in human minds.... According to this view, our minds are computers and therefore can be duplicated by other computers. Sherry Turkle writes that the "real ambition is of mythic proportions, making a general purpose intelligence, a mind." (Turkle, 1984, p. 240) The authors of a major text announce that "the ultimate goal of AI research is to build a person or, more humbly, an animal." (Charniak & McDermott, 1985, p. 7)Research in this field, like strong psychological AI, takes seriously the functionalist view that mentality can be realized in many different types of physical devices. Suprapsychological AI, however, accuses strong psychological AI of being chauvinisticof being only interested in human intelligence! Suprapsychological AI claims to be interested in all the conceivable ways intelligence can be realized. (Flanagan, 1991, pp. 241-242)16) Determination of Relevance of Rules in Particular ContextsEven if the [rules] were stored in a context-free form the computer still couldn't use them. To do that the computer requires rules enabling it to draw on just those [ rules] which are relevant in each particular context. Determination of relevance will have to be based on further facts and rules, but the question will again arise as to which facts and rules are relevant for making each particular determination. One could always invoke further facts and rules to answer this question, but of course these must be only the relevant ones. And so it goes. It seems that AI workers will never be able to get started here unless they can settle the problem of relevance beforehand by cataloguing types of context and listing just those facts which are relevant in each. (Dreyfus & Dreyfus, 1986, p. 80)Perhaps the single most important idea to artificial intelligence is that there is no fundamental difference between form and content, that meaning can be captured in a set of symbols such as a semantic net. (G. Johnson, 1986, p. 250)Artificial intelligence is based on the assumption that the mind can be described as some kind of formal system manipulating symbols that stand for things in the world. Thus it doesn't matter what the brain is made of, or what it uses for tokens in the great game of thinking. Using an equivalent set of tokens and rules, we can do thinking with a digital computer, just as we can play chess using cups, salt and pepper shakers, knives, forks, and spoons. Using the right software, one system (the mind) can be mapped into the other (the computer). (G. Johnson, 1986, p. 250)19) A Statement of the Primary and Secondary Purposes of Artificial IntelligenceThe primary goal of Artificial Intelligence is to make machines smarter.The secondary goals of Artificial Intelligence are to understand what intelligence is (the Nobel laureate purpose) and to make machines more useful (the entrepreneurial purpose). (Winston, 1987, p. 1)The theoretical ideas of older branches of engineering are captured in the language of mathematics. We contend that mathematical logic provides the basis for theory in AI. Although many computer scientists already count logic as fundamental to computer science in general, we put forward an even stronger form of the logic-is-important argument....AI deals mainly with the problem of representing and using declarative (as opposed to procedural) knowledge. Declarative knowledge is the kind that is expressed as sentences, and AI needs a language in which to state these sentences. Because the languages in which this knowledge usually is originally captured (natural languages such as English) are not suitable for computer representations, some other language with the appropriate properties must be used. It turns out, we think, that the appropriate properties include at least those that have been uppermost in the minds of logicians in their development of logical languages such as the predicate calculus. Thus, we think that any language for expressing knowledge in AI systems must be at least as expressive as the first-order predicate calculus. (Genesereth & Nilsson, 1987, p. viii)21) Perceptual Structures Can Be Represented as Lists of Elementary PropositionsIn artificial intelligence studies, perceptual structures are represented as assemblages of description lists, the elementary components of which are propositions asserting that certain relations hold among elements. (Chase & Simon, 1988, p. 490)Artificial intelligence (AI) is sometimes defined as the study of how to build and/or program computers to enable them to do the sorts of things that minds can do. Some of these things are commonly regarded as requiring intelligence: offering a medical diagnosis and/or prescription, giving legal or scientific advice, proving theorems in logic or mathematics. Others are not, because they can be done by all normal adults irrespective of educational background (and sometimes by non-human animals too), and typically involve no conscious control: seeing things in sunlight and shadows, finding a path through cluttered terrain, fitting pegs into holes, speaking one's own native tongue, and using one's common sense. Because it covers AI research dealing with both these classes of mental capacity, this definition is preferable to one describing AI as making computers do "things that would require intelligence if done by people." However, it presupposes that computers could do what minds can do, that they might really diagnose, advise, infer, and understand. One could avoid this problematic assumption (and also side-step questions about whether computers do things in the same way as we do) by defining AI instead as "the development of computers whose observable performance has features which in humans we would attribute to mental processes." This bland characterization would be acceptable to some AI workers, especially amongst those focusing on the production of technological tools for commercial purposes. But many others would favour a more controversial definition, seeing AI as the science of intelligence in general-or, more accurately, as the intellectual core of cognitive science. As such, its goal is to provide a systematic theory that can explain (and perhaps enable us to replicate) both the general categories of intentionality and the diverse psychological capacities grounded in them. (Boden, 1990b, pp. 1-2)Because the ability to store data somewhat corresponds to what we call memory in human beings, and because the ability to follow logical procedures somewhat corresponds to what we call reasoning in human beings, many members of the cult have concluded that what computers do somewhat corresponds to what we call thinking. It is no great difficulty to persuade the general public of that conclusion since computers process data very fast in small spaces well below the level of visibility; they do not look like other machines when they are at work. They seem to be running along as smoothly and silently as the brain does when it remembers and reasons and thinks. On the other hand, those who design and build computers know exactly how the machines are working down in the hidden depths of their semiconductors. Computers can be taken apart, scrutinized, and put back together. Their activities can be tracked, analyzed, measured, and thus clearly understood-which is far from possible with the brain. This gives rise to the tempting assumption on the part of the builders and designers that computers can tell us something about brains, indeed, that the computer can serve as a model of the mind, which then comes to be seen as some manner of information processing machine, and possibly not as good at the job as the machine. (Roszak, 1994, pp. xiv-xv)The inner workings of the human mind are far more intricate than the most complicated systems of modern technology. Researchers in the field of artificial intelligence have been attempting to develop programs that will enable computers to display intelligent behavior. Although this field has been an active one for more than thirty-five years and has had many notable successes, AI researchers still do not know how to create a program that matches human intelligence. No existing program can recall facts, solve problems, reason, learn, and process language with human facility. This lack of success has occurred not because computers are inferior to human brains but rather because we do not yet know in sufficient detail how intelligence is organized in the brain. (Anderson, 1995, p. 2)Historical dictionary of quotations in cognitive science > Artificial Intelligence
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18 model
1) модель (1. упрощённое представление объекта, процесса или явления; структурная аналогия 2. макет 3. образец; эталон; шаблон 4. пример; тип 5. стиль; дизайн) || моделировать (1. создавать упрощённое представление объекта, процесса или явления; пользоваться структурной аналогией 2. макетировать 3. создавать образец, эталон или шаблон 4. пользоваться примером; относить к определённому типу) || модельный (1. относящийся к упрощённому представлению объекта, процесса или явления; использующий структурную аналогию 2. макетный 3. образцовый; эталонный; шаблонный 4. примерный; типовой)2) служить моделью; выполнять функции модели3) создавать по образцу, эталону или шаблону4) придерживаться определённого стиля; следовать выбранному дизайну•- 2-D model
- adaptive expectations model
- additive model of neural network
- analog model
- antenna scale model
- application domain model
- AR model
- ARCH model
- ARDL model
- ARIMA model
- ARMA model
- atmospheric density model
- autoregressive conditional heteroscedastic model
- autoregressive distributed lags model
- autoregressive integrated moving average model
- autoregressive moving average model
- band model
- behavioral model
- Benetton model
- Berkeley short-channel IGFET model
- binary model
- binary choice model
- Bohr-Sommerfeld model
- Bohr-Sommerfeld model of atom
- Box-Jenkins model
- Bradley-Terry-Luce model
- brain-state-in-a-box model
- breadboard model
- Brookings models
- BSB model
- business model
- CAD model
- capability maturity model
- carrier-storage model
- causal model
- censored model
- centralized model
- charge-control model
- Chen model
- classical normal linear regression model
- classical regression model
- client-server model
- CMY model
- CMYK model
- cobweb model
- collective-electron model
- color model
- compact model
- component object model
- computer model
- computer-aided-design model
- conceptual model of hypercompetition
- conceptual data model
- conductor impedance model
- congruent model
- connectionist model
- continuum model
- Cox proportional hazards regression model
- data model
- Davidson-Hendry-Srba-Yeo model
- descriptive model
- design model
- deterministic model
- DHSY model
- discrete choice model
- distributed component object model
- distributed computing model
- distributed lags model
- distributed system object model
- distribution-free model
- document object model
- domain model
- domain architecture model
- duration model
- dynamic model
- EER-model
- energy-gap model
- entity-relationship model
- ER-model
- error correction model
- errors-in-variables model
- experimental model
- extended entity-relationship model
- extended relational model
- extended relational data model
- extensional model
- ferromagnetic Fermi-liquid model
- file level model
- financial model
- finite-population model
- fixed-effects model
- flat Earth model
- flat free model of advertising
- formalized model
- fractal model
- frame model
- fuzzy model
- GARCH model
- generalized autoregressive conditional heteroscedastic model
- generalized linear model
- geometric model
- geometrical lags model
- gross-level model
- ground-environment model
- Haken-Kelso-Bunz model
- Heisenberg model
- heuristic model
- hierarchical data model
- HLS model
- holographic model
- HSB model
- HSV model
- Hubbard model
- huge model
- hybrid-pi model
- hypothesis model
- ideal model
- imaging model
- indexed colors model
- information model
- information-logical model
- intensional model
- intercept-only model
- ionospheric model
- irreversible growth model
- Ising model
- ISO/OSI reference model
- Klein model
- Kronig-Penney model
- L*a*b* model
- large model
- large-signal device model
- LCH model
- learning, induction and schema abstraction model
- life cycle model
- limited dependent variable model
- linear model
- linear probability model
- LISA model
- logical model
- logical-linguistic model
- logistic model
- logit model
- loglinear model
- Londons' model of superconductivity
- lookup-table model
- Lorentz model
- low-signal device model
- machine model
- macrolevel model
- magnetic hysteresis model
- magnetohydrodynamic plasma model
- mathematical model
- matrix-memory model
- medium model
- memory model
- MHD plasma model
- microlevel model
- Minsky model
- Minsky frame model
- mixed model
- molecular-field model
- moving average model
- multiple regression model
- multiplicative model
- nested model
- network model
- network data model
- non-nested model
- non-parametric model
- N-state Potts model
- N-tier model
- null model
- object model
- object data model
- one-dimensional model
- one-fluid plasma model
- operations model
- optimizing model
- parabolic-ionosphere model
- parametric model
- parsimonious model
- partial adjustment model
- phenomenological model
- physical model
- pilot model
- Pippard nonlocal model
- plant model
- Poisson model
- polar model
- polynomial lags model
- postrelational model
- postrelational data model
- Potts model
- predictive model
- Preisach model
- preproduction model
- price model of advertising
- probabilistic model
- probit model
- proportional hazard model
- proportional-odds model
- prototype model
- quadratic model
- qualitative dependent variable model
- quantum mechanical model of superconductivity
- quasi-equilibrium model
- quasi-linear model
- random coefficients model
- random-effects model
- register model
- relational model
- relational data model
- relative model
- representative model
- response-surface model
- RGB model
- Ridley-Watkins-Hilsum model
- rival models
- Rössler model
- RWH model
- saturated model
- scalar model
- SCSI architecture model
- semantic model
- semiotic model
- sharply bounded ionosphere model
- simulation model
- single-ion model
- Skyrme model
- small model
- small-signal device model
- solid model
- spherical Earth model
- state-space model
- statistical model
- stochastic model
- Stoner-Wohlfart model
- structural model
- stuck-at-fault model
- surface model
- symbolic model
- symbolic-form model
- synergetic model
- system model
- system object model
- test model
- thermodynamical model
- three-tier model
- tobit model
- transistor model
- translog model
- tropospheric model
- true model
- truncated model
- two-dimensional model
- two-dimensional regression model
- two-fluid model of superconductivity
- two-fluid plasma model
- two-tier model
- Van der Ziel's noise model
- variable parameter model
- vector model
- wire-frame model
- working model -
19 model
1) модель (1. упрощённое представление объекта, процесса или явления; структурная аналогия 2. макет 3. образец; эталон; шаблон 4. пример; тип 5. стиль; дизайн) || моделировать (1. создавать упрощённое представление объекта, процесса или явления; пользоваться структурной аналогией 2. макетировать 3. создавать образец, эталон или шаблон 4. пользоваться примером; относить к определённому типу) || модельный (1. относящийся к упрощённому представлению объекта, процесса или явления; использующий структурную аналогию 2. макетный 3. образцовый; эталонный; шаблонный 4. примерный; типовой)2) служить моделью; выполнять функции модели3) создавать по образцу, эталону или шаблону4) придерживаться определённого стиля; следовать выбранному дизайну•- 2-D model
- adaptive expectations model
- additive model of neural network
- analog model
- antenna scale model
- application domain model
- AR model
- ARCH model
- ARDL model
- ARIMA model
- ARMA model
- atmospheric density model
- autoregressive conditional heteroscedastic model
- autoregressive distributed lags model
- autoregressive integrated moving average model
- autoregressive model
- autoregressive moving average model
- band model
- behavioral model
- Benetton model
- Berkeley short-channel IGFET model
- binary choice model
- binary model
- Bohr-Sommerfeld model of atom
- Bohr-Sommerfeld model
- Box-Jenkins model
- Bradley-Terry-Luce model
- brain-state-in-a-box model
- breadboard model
- Brookings models
- BSB model
- business model
- CAD model
- capability maturity model
- carrier-storage model
- causal model
- censored model
- centralized model
- charge-control model
- Chen model
- classical normal linear regression model
- classical regression model
- client-server model
- CMY model
- CMYK model
- cobweb model
- collective-electron model
- color model
- compact model
- component object model
- computer model
- computer-aided-design model
- conceptual data model
- conceptual model of hypercompetition
- conductor impedance model
- congruent model
- connectionist model
- continuum model
- Cox proportional hazards regression model
- data model
- Davidson-Hendry-Srba-Yeo model
- descriptive model
- design model
- deterministic model
- DHSY model
- discrete choice model
- distributed component object model
- distributed computing model
- distributed lags model
- distributed system object model
- distribution-free model
- document object model
- domain architecture model
- domain model
- duration model
- dynamic model
- EER-model
- energy-gap model
- entity-relationship model
- ER-model
- error correction model
- errors-in-variables model
- experimental model
- extended entity-relationship model
- extended relational data model
- extended relational model
- extensional model
- ferromagnetic Fermi-liquid model
- file level model
- financial model
- finite-population model
- fixed-effects model
- flat Earth model
- flat free model of advertising
- formalized model
- fractal model
- frame model
- fuzzy model
- GARCH model
- generalized autoregressive conditional heteroscedastic model
- generalized linear model
- geometric model
- geometrical lags model
- gross-level model
- ground-environment model
- Haken-Kelso-Bunz model
- Heisenberg model
- heuristic model
- hierarchical data model
- HLS model
- holographic model
- HSB model
- HSV model
- Hubbard model
- huge model
- hybrid-pi model
- hypothesis model
- ideal model
- imaging model
- indexed colors model
- information model
- information-logical model
- intensional model
- intercept-only model
- ionospheric model
- irreversible growth model
- Ising model
- ISO/OSI reference model
- Klein model
- Kronig-Penney model
- L*a*b* model
- large model
- large-signal device model
- LCH model
- learning, induction and schema abstraction model
- life cycle model
- limited dependent variable model
- linear model
- linear probability model
- LISA model
- logical model
- logical-linguistic model
- logistic model
- logit model
- loglinear model
- Londons' model of superconductivity
- lookup-table model
- Lorentz model
- low-signal device model
- machine model
- macrolevel model
- magnetic hysteresis model
- magnetohydrodynamic plasma model
- mathematical model
- matrix-memory model
- medium model
- memory model
- MHD plasma model
- microlevel model
- Minsky frame model
- Minsky model
- mixed model
- molecular-field model
- moving average model
- multiple regression model
- multiplicative model
- nested model
- network data model
- network model
- non-nested model
- non-parametric model
- N-state Potts model
- N-tier model
- null model
- object data model
- object model
- one-dimensional model
- one-fluid plasma model
- operations model
- optimizing model
- parabolic-ionosphere model
- parametric model
- parsimonious model
- partial adjustment model
- phenomenological model
- physical model
- pilot model
- Pippard nonlocal model
- plant model
- Poisson model
- polar model
- polynomial lags model
- postrelational data model
- postrelational model
- Potts model
- predictive model
- Preisach model
- preproduction model
- price model of advertising
- probabilistic model
- probit model
- proportional hazard model
- proportional-odds model
- prototype model
- quadratic model
- qualitative dependent variable model
- quantum mechanical model of superconductivity
- quasi-equilibrium model
- quasi-linear model
- random coefficients model
- random-effects model
- register model
- relational data model
- relational model
- relative model
- representative model
- response-surface model
- RGB model
- Ridley-Watkins-Hilsum model
- rival models
- Rössler model
- RWH model
- saturated model
- scalar model
- SCSI architecture model
- semantic model
- semiotic model
- sharply bounded ionosphere model
- simulation model
- single-ion model
- Skyrme model
- small model
- small-signal device model
- solid model
- spherical Earth model
- state-space model
- statistical model
- stochastic model
- Stoner-Wohlfart model
- structural model
- stuck-at-fault model
- surface model
- symbolic model
- symbolic-form model
- synergetic model
- system model
- system object model
- test model
- thermodynamical model
- three-tier model
- tobit model
- transistor model
- translog model
- tropospheric model
- true model
- truncated model
- two-dimensional model
- two-dimensional regression model
- two-fluid model of superconductivity
- two-fluid plasma model
- two-tier model
- Van der Ziel's noise model
- variable parameter model
- vector model
- wire-frame model
- working modelThe New English-Russian Dictionary of Radio-electronics > model
-
20 circuit
2) канал3) т. граф. простая цепь, контур•- active circuit
- acyclic circuit
- adding circuit
- add circuit
- addressing circuit
- advancing circuit
- alarm circuit
- amplifying circuit
- analogous circuit
- analog circuit
- AND-to-OR circuit
- antialiasing circuit
- anticoincidence circuit
- antihunting circuit
- antihunt circuit
- aperiodic circuit
- arithmetic circuit
- arithmetical circuit
- astable circuit
- averaging circuit
- balanced circuit
- basis circuit
- beam-lead integrated circuit
- benchmark circuit
- binary-valued digital circuit
- binary-valued circuit
- bipolar circuit
- bistable circuit
- blanking circuit
- bleeder circuit
- bridge circuit
- buffer circuit
- carry circuit
- character selection circuit
- checking circuit
- check circuit
- clamping circuit
- clocked circuit
- clock-recovery circuit
- closed circuit
- code disjoint circuit
- coincidence circuit
- combinational circuit
- combinatorial circuit
- communication circuit
- comparator circuit
- compare circuit
- comparison circuit
- complementary circuit
- complementary integrated circuit
- complementary transistor logic circuit
- complex function circuit
- computer circuit
- computer test circuit
- computing circuit
- control circuit
- core-diode circuit
- core-transistor circuit
- correcting circuit
- correction circuit
- counter circuit
- counting circuit
- coupling circuit
- current-limit circuit
- current-operated circuit
- current-summation circuit
- custom product integrated circuit
- custom integrated circuit
- custom-wired integrated circuit
- cutoff circuit
- cycle circuit
- cyclic circuit
- dead-on-arrival integrated circuit
- decode circuit
- decoding circuit
- deenergizing circuit
- deflection circuit
- delay circuit
- densely packed circuit
- differentiating circuit
- digital computing circuit
- diode circuit
- diode-coupled circuit
- diode-transistor logic circuit
- direct-coupled circuit
- direct-coupled transistor logic circuit
- direct-current circuit
- discrete component circuit
- discrete logic-level
- discrete wired circuit
- display circuit
- divide-by-two circuit
- dividing circuit
- double-sided printed circuit
- doubling circuit
- drive circuit
- dry circuit
- dual circuit
- duplex circuit
- Eccles-Jordan circuit
- edge-activated circuit
- emitter-coupled circuit
- emitter-coupled logic circuit
- emitter-emitter-coupled logic circuit
- equality circuit
- equivalent circuit
- etched circuit
- Euler circuit
- except circuit
- fanout-free circuit
- fast-switching circuit
- fault detection circuit
- fault-free circuit
- fault-secure circuit
- faulty circuit
- feedback circuit
- ferrite-diode circuit
- ferrite-transistor circuit
- ferroresonant computing circuit
- film integrated circuit
- flag-testing circuit
- flat-pack integrated circuit
- flexible printed circuit
- flexible circuit
- flip-chip integrated circuit
- flip-flop circuit
- frame-grounding circuit
- frequency-halving circuit
- function circuit
- gate circuit
- Goto-pair circuit
- half-duplex circuit
- halving circuit
- Hamilton circuit
- hand-designed circuit
- hardwired circuit
- high-speed circuit
- high-threshold logic circuit
- holding circuit
- hybrid circuit
- idler circuit
- imbedded circuit
- IMOS circuit
- impulse circuit
- inhibit circuit
- input circuit
- integrated circuit
- integrating circuit
- integro-differential circuit
- interchange circuit
- interface circuit
- interfacing circuit
- interlock circuit
- invert circuit
- ion-implanted MOS circuit
- irredundant circuit
- Josephson integrated circuit
- junction transistor circuit
- ladder circuit
- lag-lead circuit
- laminar circuit
- large arithmetic circuit
- large-scale integrated circuit
- large-scale integration circuit
- latch circuit
- lead-lag circuit
- leased circuit
- level circuit
- linear circuit
- linear integrated circuit
- linearity circuit
- liquid logic circuit
- load circuit
- locked pair circuit
- locking circuit
- logic circuit
- logical circuit
- low-threshold integrated circuit
- LSI circuit
- lumped circuit
- magnetic circuit
- magnetic-core circuit
- majority circuit
- match circuit
- material equivalence circuit
- matrix circuit
- maximum-remembering circuit
- measuring circuit
- medium-scale integration circuit
- memory circuit
- memory-decoder circuit
- message circuit
- metal-oxide-semiconductor circuit
- microamp circuit
- microelectronic integrated circuit
- microminiature circuit
- microwave circuit
- mil spec integrated circuit
- milliwatt circuit
- miniature circuit
- minimum-remembering circuit
- mixed-level circuit
- mixing circuit
- modularized circuit
- molecular integrated circuit
- monitoring circuit
- monolithic integrated circuit
- monostable circuit
- MOS circuit
- MOS integrated circuit
- MOS LSI circuit
- MSI circuit
- multichip integrated circuit
- multifunction integrated circuit
- multilayer circuit
- multilevel circuit
- multiple output circuit
- multiplying circuit
- multipoint circuit
- multistable circuit
- multistage circuit
- nanosecond circuit
- n-channel circuit
- network circuit
- noise-balancing circuit
- noncoincidence circuit
- noncutoff circuit
- non-self-checking circuit
- one-core-per-bit circuit
- one-generator equivalent circuit
- one-out-of-four selecting circuit
- one-shot circuit
- open circuit
- optical commutation circuit
- optical memory circuit
- optically coupled circuit
- optoelectronic circuit
- output circuit
- packaged circuit
- packed circuit
- p-channel circuit
- phantom circuit
- phase-comparison circuit
- phase-inverting circuit
- picosecond circuit
- pilot circuit
- plastic-embedded circuit
- point-to-point circuit
- power circuit
- power monitoring circuit
- power-fail circuit
- printed circuit
- priority circuit
- propagation circuit
- protection circuit
- pulse circuit
- pulse-actuated circuit
- pulse-broadening circuit
- pulse-regenerating circuit
- pulse-shaping circuit
- pulse-stretching circuit
- pulse-switching circuit
- pumped tunnel-diode transistor logic circuit
- pumping circuit
- quenching circuit
- race-free circuit
- radio-frequency circuit
- random-logic circuit
- ratioed circuit
- reading circuit
- received-data circuit
- receiving circuit
- reconfigurable integrated circuit
- redundant circuit
- reference circuit
- refreshing circuit
- relaxation circuit
- reset circuit
- retriggerable circuit
- rewriting circuit
- ring circuit
- rounding circuit
- sample-hold circuit
- saturated circuit
- scale-of-N circuit
- scale-of-two circuit
- scaling circuit
- schematic circuit
- Schmitt trigger circuit
- Schmitt circuit
- screen printed circuit
- selection circuit
- select circuit
- self-checking circuit
- self-testing circuit
- self-timed circuit
- semiconductor circuit
- send-request circuit
- sequential circuit
- shifting circuit
- shift circuit
- short circuit
- shunt-peaking circuit
- sign-controlled circuit
- silicon integrated circuit
- silicon-on-sapphire integrated circuit
- simplex circuit
- single-chip circuit
- single-ended circuit
- single-level circuit
- single-phase circuit
- single-shot circuit
- small-scale integration circuit
- solid-state circuit
- solid circuit
- SOS integrated circuit
- squaring circuit
- SSI circuit
- stabilizing circuit
- stamped circuit
- start-stop circuit
- steering circuit
- storage circuit
- storage-selection circuit
- strongly fault-secure circuit
- subtraction circuit
- summing circuit
- sweep circuit
- switching circuit
- symbolic circuit
- synchronizing circuit
- synthesis circuit
- thick-film circuit
- thin-film circuit
- threshold circuit
- time-anticoincidence circuit
- time-base circuit
- time-coincidence circuit
- time-delay circuit
- toll circuit
- totally self-checking circuit
- transistor circuit
- transistor-core circuit
- transistor-resistor circuit
- transistor-transistor-logic circuit
- translation circuit
- transmitted-data circuit
- transmitting circuit
- tree circuit
- trigger -action circuit
- trigger circuit
- trunk circuit
- tunnel diode circuit
- twin-tunnel-diode circuit
- twin circuit
- two-cores-per-bit circuit
- two-input circuit
- two-level circuit
- two-way circuit
- ultra-large-scale integration circuit
- unidirectional circuit
- unpackaged circuit
- unpacked circuit
- very-high-speed integrated circuit
- very-large-scale integration circuit
- virtual circuit
- VLSI circuit
- voice circuit
- voice-grade circuit
- voltage-control circuit
- voltage-doubling circuit
- voltage-multiplying circuit
- voltage-summation circuit
- voter circuit
- wave-shaping circuit
- whole-wafer circuit
- wired AND circuit
- wired OR circuit
- wire-wrapped circuit
- writing circuit
- zero circuitEnglish-Russian dictionary of computer science and programming > circuit
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