This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1250grams, ISBN:0792326431.

Bestandsnummer des Verk�ufers 4137500

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Bibliografische Details

Titel

Fuzzy Reasoning in Information, Decision and Control Systems

Verlag

Springer

Erscheinungsjahr

1994

Sprache

Englisch

ISBN-10

0792326431

ISBN-13

9780792326434

Einband

Hardcover

Zustand

Good

Gewicht

1250 Gramm

Verk�uferkataloge

Engineering

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Inhaltsangabe

Great progresses have been made in the application of fuzzy set theory and fuzzy logic. Most remarkable area of application is ’fuzzy control’, where fuzzy logic was first applied to plant control systems and its use is expanding to consumer products. Most of fuzzy control systems uses fuzzy inference with max-min or max-product composition, similar to the algorithm that first used by Mamdani in 1970s. Some algorithms are developed to refine fuzzy controls systems but the main part of algorithm stays the same. Triggered by the success of fuzzy control systems, other ways of applying fuzzy set theory are also investigated. They are usually referred to as ’fuzzy expert sys� tems’, and their purpose are to combine the idea of fuzzy theory with AI based approach toward knowledge processing. These approaches can be more generally viewed as ’fuzzy information processing’, that is to bring fuzzy idea into informa� tion processing systems.

Rese�a del editor

Great progresses have been made in the application of fuzzy set theory and fuzzy logic. Most remarkable area of application is 'fuzzy control', where fuzzy logic was first applied to plant control systems and its use is expanding to consumer products. Most of fuzzy control systems uses fuzzy inference with max-min or max-product composition, similar to the algorithm that first used by Mamdani in 1970s. Some algorithms are developed to refine fuzzy controls systems but the main part of algorithm stays the same. Triggered by the success of fuzzy control systems, other ways of applying fuzzy set theory are also investigated. They are usually referred to as 'fuzzy expert sys� tems', and their purpose are to combine the idea of fuzzy theory with AI based approach toward knowledge processing. These approaches can be more generally viewed as 'fuzzy information processing', that is to bring fuzzy idea into informa� tion processing systems.

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