Fuzzy Reasoning in Information, Decision and Control Systems

Anastasios N. Venetsanopoulos

ISBN 10: 9401740828 / ISBN 13: 9789401740821

Published by Springer Netherlands, Springer Netherlands, 2013

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9789401740821

Bibliographic Details

• Title: Fuzzy Reasoning in Information, Decision and Control Systems
• Publisher: Springer Netherlands, Springer Netherlands
• Publication Date: 2013
• Language: English
• ISBN 10: 9401740828
• ISBN 13: 9789401740821
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 32D millimeters 235 millimeters width by 155 millimeters height by 32 millimeters depth
• Weight: 885 grams

About this title

Synopsis

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.