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© 2014

System Identification and Adaptive Control

Theory and Applications of the Neurofuzzy and Fuzzy Cognitive Network Models

Book

Part of the Advances in Industrial Control book series (AIC)

Table of contents

  1. Front Matter
    Pages i-xii
  2. The Recurrent Neurofuzzy Model

    1. Front Matter
      Pages 1-1
    2. Yiannis Boutalis, Dimitrios Theodoridis, Theodore Kottas, Manolis A. Christodoulou
      Pages 3-23
    3. Yiannis Boutalis, Dimitrios Theodoridis, Theodore Kottas, Manolis A. Christodoulou
      Pages 25-55
    4. Yiannis Boutalis, Dimitrios Theodoridis, Theodore Kottas, Manolis A. Christodoulou
      Pages 57-85
    5. Yiannis Boutalis, Dimitrios Theodoridis, Theodore Kottas, Manolis A. Christodoulou
      Pages 87-118
    6. Yiannis Boutalis, Dimitrios Theodoridis, Theodore Kottas, Manolis A. Christodoulou
      Pages 119-159
    7. Yiannis Boutalis, Dimitrios Theodoridis, Theodore Kottas, Manolis A. Christodoulou
      Pages 161-181
  3. The FCN Model

    1. Front Matter
      Pages 183-183
    2. Yiannis Boutalis, Dimitrios Theodoridis, Theodore Kottas, Manolis A. Christodoulou
      Pages 185-196
    3. Yiannis Boutalis, Dimitrios Theodoridis, Theodore Kottas, Manolis A. Christodoulou
      Pages 197-214
    4. Yiannis Boutalis, Dimitrios Theodoridis, Theodore Kottas, Manolis A. Christodoulou
      Pages 215-249
    5. Yiannis Boutalis, Dimitrios Theodoridis, Theodore Kottas, Manolis A. Christodoulou
      Pages 251-306
  4. Back Matter
    Pages 307-313

About this book

Introduction

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:

•             contemporary power generation;

•             process control; and

•             conventional benchmarking problems.

Researchers and graduate students working in adaptive estimation and intelligent control will find Neurofuzzy Adaptive Control of interest both for the currency of its models and because it demonstrates their relevance for real systems. The monograph also shows industrial engineers how to test intelligent adaptive control easily using proven theoretical results.

Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Keywords

Adaptive Control Adaptive Estimation Fuzzy Cognitive Maps Fuzzy Cognitive Networks Neurofuzzy Models

Authors and affiliations

  1. 1.Department of Electrical and Computer EngineeringDemocritus University of ThraceXanthiGreece
  2. 2.Department of Electrical and Computer EngineeringDemocritus University of ThraceXanthiGreece
  3. 3.Department of Electrical and Computer EngineeringDemocritus University of ThraceXanthiGreece
  4. 4.KifisiaGreece

About the authors

The authors works intensively in the field of intelligent control and its applications. Two of them (Boutalis and Christodoulou) are professors with long research experience and the other two are relevantly young researchers with a significant number of publications in the area of intelligent control.

Bibliographic information

  • Book Title System Identification and Adaptive Control
  • Book Subtitle Theory and Applications of the Neurofuzzy and Fuzzy Cognitive Network Models
  • Authors Yiannis Boutalis
    Dimitrios Theodoridis
    Theodore Kottas
    Manolis A. Christodoulou
  • Series Title Advances in Industrial Control
  • Series Abbreviated Title Advances in Industrial Control
  • DOI https://doi.org/10.1007/978-3-319-06364-5
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Hardcover ISBN 978-3-319-06363-8
  • Softcover ISBN 978-3-319-35412-5
  • eBook ISBN 978-3-319-06364-5
  • Series ISSN 1430-9491
  • Series E-ISSN 2193-1577
  • Edition Number 1
  • Number of Pages XII, 313
  • Number of Illustrations 64 b/w illustrations, 56 illustrations in colour
  • Topics Control and Systems Theory
    Artificial Intelligence
    Computational Intelligence
    Industrial and Production Engineering
  • Buy this book on publisher's site
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