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ετυμολογία

fuzzy logics - translation to ρωσικά

SYSTEM FOR REASONING ABOUT VAGUENESS

Fuzzy Logic; Fuzzy logician; Formal fuzzy logic; Fuzzy transportation; Fuzzy Transportation; Fuzzy logics; Fuzzy databases; Applications of fuzzy logic; Compensatory fuzzy logic; Fuzzy inference; Fuzzy inference system; Linguistic variable (fuzzy logic); Zadeh operator; Fuzzy Turing machine; Fuzzy database; Fuzzy relational database; Propositional fuzzy logic; Gödel fuzzy logic; Product fuzzy logic; Probability and fuzzy logic; Fuzzy logic and probability

fuzzy logics

[лог.] размытая /нечёткая/ логика

fuzzy logic

общая лексика

нечёткая логика

формальная система логики, разработанная Лотфи-заде (Lotfi Zadeh, университет Беркли) в 60-х годах, являющаяся расширением обычной Булевой логики. В ней значения "истина" и "ложь" заменяются значениями функции на отрезке [0, 1] (концепция частичной правды). Позволяет уйти от однозначности ответа на вопрос. Часто используется в экспертных и самообучающихся системах и системах распознавания образов

математика

нечеткая (размытая) логика

Смотрите также

binary logic; DFP; fuzzy computing; ternary logic

fuzzy logic

нечеткая логика

Ορισμός

fuzzy logic

A superset of Boolean logic dealing with the concept of partial truth -- truth values between "completely true" and "completely false". It was introduced by Dr. Lotfi Zadeh of UCB in the 1960's as a means to model the uncertainty of natural language. Any specific theory may be generalised from a discrete (or "crisp") form to a continuous (fuzzy) form, e.g. "fuzzy calculus", "fuzzy differential equations" etc. Fuzzy logic replaces Boolean truth values with degrees of truth which are very similar to probabilities except that they need not sum to one. Instead of an assertion pred(X), meaning that X definitely has the property associated with predicate "pred", we have a truth function truth(pred(X)) which gives the degree of truth that X has that property. We can combine such values using the standard definitions of fuzzy logic: truth(not x) = 1.0 - truth(x) truth(x and y) = minimum (truth(x), truth(y)) truth(x or y) = maximum (truth(x), truth(y)) (There are other possible definitions for "and" and "or", e.g. using sum and product). If truth values are restricted to 0 and 1 then these functions behave just like their Boolean counterparts. This is known as the "extension principle". Just as a Boolean predicate asserts that its argument definitely belongs to some subset of all objects, a fuzzy predicate gives the degree of truth with which its argument belongs to a fuzzy subset. Usenet newsgroup: news:comp.ai.fuzzy. E-mail servers: <fuzzynet@aptronix.com>, <rnalib@its.bldrdoc.gov>, <fuzzy-server@til.com>. ftp://ftp.hiof.no/pub/Fuzzy, ftp://ntia.its.bldrdoc.gov/pub/fuzzy. {FAQ (ftp://rtfm.mit.edu/pub/usenet-by-group/comp.answers/fuzzy-logic)}. {James Brule, "Fuzzy systems - a tutorial", 1985 (http://life.anu.edu.au/complex_systems/fuzzy.html)}. {STB Software Catalog (http://krakatoa.jsc.nasa.gov/stb/catalog.html)}, includes a few fuzzy tools. [H.J. Zimmerman, "Fuzzy Sets, Decision Making and Expert Systems", Kluwer, Dordrecht, 1987]. ["Fuzzy Logic, State of the Art", Ed. R. Lowen, Marc Roubens, Theory and Decision Library, D: System theory, Knowledge Engineering and Problem Solving 12, Kluwer, Dordrecht, 1993, ISBN 0-7923-2324-6]. (1995-02-21)

Εμφάνιση περισσότερων ορισμών

Βικιπαίδεια

Fuzzy logic

Fuzzy logic is a form of many-valued logic in which the truth value of variables may be any real number between 0 and 1. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. By contrast, in Boolean logic, the truth values of variables may only be the integer values 0 or 1.

The term fuzzy logic was introduced with the 1965 proposal of fuzzy set theory by Iranian Azerbaijani mathematician Lotfi Zadeh. Fuzzy logic had, however, been studied since the 1920s, as infinite-valued logic—notably by Łukasiewicz and Tarski.

Fuzzy logic is based on the observation that people make decisions based on imprecise and non-numerical information. Fuzzy models or fuzzy sets are mathematical means of representing vagueness and imprecise information (hence the term fuzzy). These models have the capability of recognising, representing, manipulating, interpreting, and using data and information that are vague and lack certainty.

Fuzzy logic has been applied to many fields, from control theory to artificial intelligence.

https://en.wikipedia.org/wiki/Fuzzy logic

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