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Adaptive Nonlinear System Identification

The Volterra and Wiener Model Approaches

  • Tokunbo¬†Ogunfunmi

Part of the Signals And Communication Technology book series (SCT)

About this book

Introduction

Adaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches introduces engineers and researchers to the field of nonlinear adaptive system identification. The book includes recent research results in the area of adaptive nonlinear system identification and presents simple, concise, easy-to-understand methods for identifying nonlinear systems. These methods use adaptive filter algorithms that are well known for linear systems identification. They are applicable for nonlinear systems that can be efficiently modeled by polynomials.

After a brief introduction to nonlinear systems and to adaptive system identification, the author presents the discrete Volterra model approach. This is followed by an explanation of the Wiener model approach. Adaptive algorithms using both models are developed. The performance of the two methods are then compared to determine which model performs better for system identification applications.

Adaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches is useful to graduates students, engineers and researchers in the areas of nonlinear systems, control, biomedical systems and in adaptive signal processing.

Keywords

Adaptive Filters Filter Nonlinear Systems Nonlinear system Performance Signal Processing System Volterra Model Wiener Model system identification

Authors and affiliations

  • Tokunbo¬†Ogunfunmi
    • 1
  1. 1.Santa Clara UniversitySanta ClaraUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-68630-1
  • Copyright Information Springer-Verlag US 2007
  • Publisher Name Springer, Boston, MA
  • eBook Packages Engineering
  • Print ISBN 978-0-387-26328-1
  • Online ISBN 978-0-387-68630-1
  • Series Print ISSN 1860-4862
  • Buy this book on publisher's site