Evolving Fuzzy Systems – Methodologies, Advanced Concepts and Applications

  • Edwin Lughofer

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 266)

Table of contents

  1. Front Matter
  2. Introduction

    1. Edwin Lughofer
      Pages 1-42
  3. Part I: Basic Methodologies

    1. Front Matter
      Pages 43-43
    2. Edwin Lughofer
      Pages 45-91
  4. Part II: Advanced Concepts

    1. Front Matter
      Pages 165-165
    2. Edwin Lughofer
      Pages 167-212
    3. Edwin Lughofer
      Pages 261-291
  5. Part III: Applications

    1. Front Matter
      Pages 293-293
    2. Edwin Lughofer
      Pages 295-323
    3. Edwin Lughofer
      Pages 325-355
    4. Edwin Lughofer
      Pages 357-391
    5. Edwin Lughofer
      Pages 393-410
  6. Back Matter

About this book


In today’s real-world applications, there is an increasing demand of integrating new information and knowledge on-demand into model building processes to account for changing system dynamics, new operating conditions, varying human behaviors or environmental influences.

Evolving fuzzy systems (EFS) are a powerful tool to cope with this requirement, as they are able to automatically adapt parameters, expand their structure and extend their memory on-the-fly, allowing on-line/real-time modeling.

This book comprises several evolving fuzzy systems approaches which have emerged during the last decade and highlights the most important incremental learning methods used. The second part is dedicated to advanced concepts for increasing performance, robustness, process-safety and reliability, for enhancing user-friendliness and enlarging the field of applicability of EFS and for improving the interpretability and understandability of the evolved models. The third part underlines the usefulness and necessity of evolving fuzzy systems in several online real-world application scenarios, provides an outline of potential future applications and raises open problems and new challenges for the next generation evolving systems, including human-inspired evolving machines.

The book includes basic principles, concepts, algorithms and theoretic results underlined by illustrations.  It is dedicated to researchers from the field of fuzzy systems, machine learning, data mining and system identification as well as engineers and technicians who apply data-driven modeling techniques in real-world systems.


Computational Intelligence Evolving Fuzzy Systems Incremental Learning On-line Modeling

Authors and affiliations

  • Edwin Lughofer
    • 1
  1. 1.Fuzzy Logic Laboratorium Linz-HagenbergSoftwarepark Hagenberg Hagenberg

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2011
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-642-18086-6
  • Online ISBN 978-3-642-18087-3
  • Series Print ISSN 1434-9922
  • Series Online ISSN 1860-0808
  • Buy this book on publisher's site
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