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Nonlinear Modeling

Advanced Black-Box Techniques

  • Johan A. K. Suykens
  • Joos Vandewalle

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Lee A. Feldkamp, Danil V. Prokhorov, Charles F. Eagen, Fumin Yuan
    Pages 29-53
  3. Yi Yu, Wayne Lawton, Seng Luan Lee, Shaohua Tan, Joos Vandewalle
    Pages 119-148
  4. Vincent Wertz, Stephen Yurkovich
    Pages 149-175
  5. Ulrich Parlitz
    Pages 209-239
  6. Johan A. K. Suykens, Joos Vandewalle
    Pages 241-253
  7. Back Matter
    Pages 255-256

About this book

Introduction

Nonlinear Modeling: Advanced Black-Box Techniques discusses methods on
  • Neural nets and related model structures for nonlinear system identification;
  • Enhanced multi-stream Kalman filter training for recurrent networks;
  • The support vector method of function estimation;
  • Parametric density estimation for the classification of acoustic feature vectors in speech recognition;
  • Wavelet-based modeling of nonlinear systems;
  • Nonlinear identification based on fuzzy models;
  • Statistical learning in control and matrix theory;
  • Nonlinear time-series analysis.

It also contains the results of the K.U. Leuven time series prediction competition, held within the framework of an international workshop at the K.U. Leuven, Belgium in July 1998.

Keywords

Nonlinear system cognition filter filters model modeling network speech recognition system system identification

Editors and affiliations

  • Johan A. K. Suykens
    • 1
  • Joos Vandewalle
    • 1
  1. 1.Katholieke Universiteit LeuvenBelgium

Bibliographic information