Electronic Resource

System Identification and Adaptive Control: Theory and Applications of the Neurofuzzy and Fuzzy Cognitive Network Models

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Penilaian

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Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented. Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN model stems from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering systems. All chapters are supported by illustrative simulation experiments, while separate chapters are devoted to the potential industrial applications of each model including projects in:

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Availability

Inventory Code	Barcode	Call Number	Location	Status
1408000256	EB0000420	629.836 Bou s	Central Library	Available

Detail Information

Series Title

Advances in Industrial Control

Call Number

629.836 Bou s

Publisher

Switzerland : Springer Cham., 2014

Collation

xii, 313p.: III.

Language

English

ISBN/ISSN

978-3-319-06364-5

Classification

629.836

Content Type

-

Media Type

-

Carrier Type

-

Edition

1

Subject(s)

Artificial intelligence
Industrial and Production Engineering

Specific Detail Info

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Statement of Responsibility

RIZAL

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