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Nonlinear System Identification

From Classical Approaches to Neural Networks and Fuzzy Models

  • Oliver Nelles

Table of contents

  1. Front Matter
    Pages I-XVII
  2. Introduction

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      Pages 1-19
  3. Optimization Techniques

    1. Front Matter
      Pages 21-21
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      Pages 23-34
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      Pages 35-77
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      Pages 79-112
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    8. Back Matter
      Pages 203-205
  4. Static Models

    1. Front Matter
      Pages 207-207
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      Pages 341-389
    7. Back Matter
      Pages 451-453
  5. Dynamic Models

    1. Front Matter
      Pages 455-455
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  6. Applications

    1. Front Matter
      Pages 653-653
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      Pages 709-733
  7. Back Matter
    Pages 735-785

About this book

Introduction

The goal of this book is to provide engineers and scientIsts in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. The reader will be able to apply the discussed models and methods to real problems with the necessary confidence and the awareness of potential difficulties that may arise in practice. This book is self-contained in the sense that it requires merely basic knowledge of matrix algebra, signals and systems, and statistics. Therefore, it also serves as an introduction to linear system identification and gives a practical overview on the major optimization methods used in engineering. The emphasis of this book is on an intuitive understanding of the subject and the practical application of the discussed techniques. It is not written in a theorem/proof style; rather the mathematics is kept to a minimum and the pursued ideas are illustrated by numerous figures, examples, and real-world applications. Fifteen years ago, nonlinear system identification was a field of several ad-hoc approaches, each applicable only to a very restricted class of systems. With the advent of neural networks, fuzzy models, and modern structure opti­ mization techniques a much wider class of systems can be handled. Although one major characteristic of nonlinear systems is that almost every nonlinear system is unique, tools have been developed that allow the use of the same ap­ proach for a broad variety of systems.

Keywords

Automatisierungstechnik Fuzzy fuzzy system linear optimization model optimization statistics

Authors and affiliations

  • Oliver Nelles
    • 1
  1. 1.UC Berkeley / TU DarmstadtKronbergGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-662-04323-3
  • Copyright Information Springer-Verlag Berlin Heidelberg 2001
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-642-08674-8
  • Online ISBN 978-3-662-04323-3
  • Buy this book on publisher's site