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Nonlinear System Identification

From Classical Approaches to Neural Networks and Fuzzy Models

  • Textbook
  • © 2001

Overview

Authors:
  1. Oliver Nelles

    1. UC Berkeley / TU Darmstadt, Kronberg, Germany

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  • Easy and intuitive understanding
  • Explanations and terminology from an engineering point-of-view
  • Only basic mathematics required
  • Self-contained, no other literature needed
  • Includes supplementary material: sn.pub/extras

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Table of contents (22 chapters)

  1. Front Matter

    Pages I-XVII
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  2. Introduction

    1. Introduction

      • Oliver Nelles
      Pages 1-19

  3. Optimization Techniques

    1. Front Matter

      Pages 21-21
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    2. Introduction to Optimization

      • Oliver Nelles
      Pages 23-34

    3. Linear Optimization

      • Oliver Nelles
      Pages 35-77

    4. Nonlinear Local Optimization

      • Oliver Nelles
      Pages 79-112

    5. Nonlinear Global Optimization

      • Oliver Nelles
      Pages 113-136

    6. Unsupervised Learning Techniques

      • Oliver Nelles
      Pages 137-155

    7. Model Complexity Optimization

      • Oliver Nelles
      Pages 157-201

  4. Back Matter

    Pages 203-205
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  5. Static Models

    1. Front Matter

      Pages 207-207
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    2. Introduction to Static Models

      • Oliver Nelles
      Pages 209-217

    3. Linear, Polynomial, and Look-Up Table Models

      • Oliver Nelles
      Pages 219-238

    4. Neural Networks

      • Oliver Nelles
      Pages 239-297

    5. Fuzzy and Neuro-Fuzzy Models

      • Oliver Nelles
      Pages 299-340

    6. Local Linear Neuro-Fuzzy Models: Fundamentals

      • Oliver Nelles
      Pages 341-389

    7. Local Linear Neuro-Fuzzy Models: Advanced Aspects

      • Oliver Nelles
      Pages 391-449

  6. Back Matter

    Pages 451-453
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  7. Dynamic Models

    1. Front Matter

      Pages 455-455
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    2. Linear Dynamic System Identification

      • Oliver Nelles
      Pages 457-546
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Keywords

About this book

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.

Authors and Affiliations

  • UC Berkeley / TU Darmstadt, Kronberg, Germany

    Oliver Nelles

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