Fuzzy Reasoning in Information, Decision and Control Systems (Intelligent Systems, Control and Automation: Science and Engineering)

Tzafestas, S.G. (Editor) / Venetsanopoulos, Anastasios N. (Editor)

ISBN 10: 9401740828 / ISBN 13: 9789401740821

Edité par Springer, 2013

A propos de cet article

592 pages. 9.21x6.14x1.20 inches. In Stock.

N° de réf. du vendeur x-9401740828

Détails bibliographiques

• Titre: Fuzzy Reasoning in Information, Decision and Control Systems (Intelligent Systems, Control and Automation: Science and Engineering)
• Éditeur: Springer
• Date de parution: 2013
• Langue: anglais
• ISBN à 10 chiffres: 9401740828
• ISBN à 13 chiffres: 9789401740821
• Reliure: Paperback
• État de l'article: Brand New
• Poids: 0,9 kilogramme

A propos de ce titre

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.