Fuzzy Reasoning in Information, Decision and Control Systems

Anastasios N. Venetsanopoulos

ISBN 10: 0792326431 / ISBN 13: 9780792326434

Verlag: Springer Netherlands, Springer Netherlands Feb 1994, 1994

Beschreibung

This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 592 pp. Englisch.

Bestandsnummer des Verkäufers 9780792326434

Bibliografische Details

• Titel: Fuzzy Reasoning in Information, Decision and Control Systems
• Verlag: Springer Netherlands, Springer Netherlands Feb 1994
• Erscheinungsjahr: 1994
• Sprache: Englisch
• ISBN-10: 0792326431
• ISBN-13: 9780792326434
• Einband: Buch
• Zustand: Neu
• Abmessungen: 241B x 160 H x 37 T Millimeter Breite: 241 mm, Höhe: 160 mm, Tiefe: 37 mm
• Gewicht: 1045 Gramm

Über diesen Titel

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.