Table of contents

  1. Front Matter
  2. Andrzej Janczak
    Pages 1-30
  3. Andrzej Janczak
    Pages 31-75
  4. Andrzej Janczak
    Pages 77-116
  5. Andrzej Janczak
    Pages 117-141
  6. Andrzej Janczak
    Pages 143-157
  7. Andrzej Janczak
    Pages 159-185
  8. Back Matter

About this book

Introduction

This monograph systematically presents the existing identification methods of nonlinear systems using the block-oriented approach It surveys various known approaches to the identification of Wiener and Hammerstein systems which are applicable to both neural network and polynomial models. The book gives a comparative study of their gradient approximation accuracy, computational complexity, and convergence rates and furthermore presents some new and original methods concerning the model parameter adjusting with gradient-based techniques. "Identification of Nonlinear Systems Using Neural Networks and Polynomal Models" is useful for researchers, engineers and graduate students in nonlinear systems and neural network theory.

Keywords

Justin Learning Algorithms Nonlinear Systems Polynomial Models neural networks nonlinear system system identification

Bibliographic information

  • DOI https://doi.org/10.1007/b98334
  • Copyright Information Springer-Verlag Berlin/Heidelberg 2005
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-23185-1
  • Online ISBN 978-3-540-31596-4
  • Series Print ISSN 0170-8643
  • Series Online ISSN 1610-7411
  • About this book