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Principles of Adaptive Filters and Self-learning Systems

  • Anthony Zaknich
  • Michael J. Grimble
  • Michael A. Johnson

Table of contents

  1. Front Matter
    Pages i-xxii
  2. Introduction

  3. Modelling

  4. Classical Filters and Spectral Analysis

  5. Adaptive Filter Theory

  6. Nonclassical Adaptive Systems

  7. Adaptive Filter Application

  8. Back Matter
    Pages 373-386

About this book

Introduction

Kalman and Wiener Filters, Neural Networks, Genetic Algorithms and Fuzzy Logic Systems Together in One Text Book

How can a signal be processed for which there are few or no a priori data?

Professor Zaknich provides an ideal textbook for one-semester introductory graduate or senior undergraduate courses in adaptive and self-learning systems for signal processing applications. Important topics are introduced and discussed sufficiently to give the reader adequate background for confident further investigation. The material is presented in a progression from a short introduction to adaptive systems through modelling, classical filters and spectral analysis to adaptive control theory, nonclassical adaptive systems and applications.

 

Features:

• Comprehensive review of linear and stochastic theory.

• Design guide for practical application of the least squares estimation method and Kalman filters.

• Study of classical adaptive systems together with neural networks, genetic algorithms and fuzzy logic systems and their combination to deal with such complex problems as underwater acoustic signal processing.

• Tutorial problems and exercises which identify the significant points and demonstrate the practical relevance of the theory.

• PDF Solutions Manual, available to tutors from springeronline.com, containing not just answers to the tutorial problems but also course outlines, sample examination material and project assignments to help in developing a teaching programme and to give ideas for practical investigations.

Keywords

Adaptive Filter Signal adaptive control algorithms control filtering filters fuzzy fuzzy logic genetic algorithms logic modeling network neural networks signal processing

Authors and affiliations

  • Anthony Zaknich
    • 1
    • 2
  1. 1.School of Engineering Science, Rockingham CampusMurdoch UniversityMurdochAustralia
  2. 2.Centre for Intelligent Information Processing Systems, School of Electrical, Electronic and Computer EngineeringThe University of Western AustraliaCrawleyAustralia

Editors and affiliations

  • Michael J. Grimble
    • 1
  • Michael A. Johnson
    • 1
  1. 1.Industrial Control Centre, Department of Electronic and Electrical EngineeringUniversity of StrathclydeGlasgowUK

Bibliographic information

  • DOI https://doi.org/10.1007/b138890
  • Copyright Information Springer-Verlag London Limited 2005
  • Publisher Name Springer, London
  • eBook Packages Engineering
  • Print ISBN 978-1-85233-984-5
  • Online ISBN 978-1-84628-121-1
  • Series Print ISSN 1439-2232
  • Buy this book on publisher's site