Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models / Edition 1

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More About This Textbook

Overview

Written from an engineering point of view, this book covers the most common and important approaches for the identification of nonlinear static and dynamic systems. The book also provides the reader with the necessary background on optimization techniques, making it fully self-contained. The new edition includes exercises.

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Product Details

  • ISBN-13: 9783540673699
  • Publisher: Springer-Verlag New York, LLC
  • Publication date: 12/12/2000
  • Edition description: 2001
  • Edition number: 1
  • Pages: 802
  • Product dimensions: 1.75 (w) x 6.14 (h) x 9.21 (d)

Table of Contents

Introduction; Introduction to Optimization; Linear Optimization; Nonlinear Local Optimization; Nonlinear Global Optimization; Unsupervised Learning Techniques; Model Complexity Optimization; Summary of Part I; Introduction to Static Models; Linear, Polynomial, and Look-up Table Models; Neural Networks; Fuzzy and Neuro-Fuzzy Models; Local Linear Neuro-Fuzzy — Fundamentals; Local Linear Neuro-Fuzzy Models — Advanced Aspects; Summary of Part II; Linear Dynamic System Indentification; Nonlinear Dynamic System Identification; Classical Polynomial Approaches; Dynamic Neural and Fuzzy Models; Dynamic Local Linear Neuro-Fuzzy Models; Neural Networks with Internal Dynamics; Part IV: Applications; Applications of Static Models; Applications of Dynamic Models; Applications of Advanced Methods; Vectors and Matrices; Statistics.

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