Identification of Nonlinear Systems Using Neural Networks and Polynomial Models: A Block-Oriented Approach

by Andrzej Janczak

Overview

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.

Product Details

ISBN-13:	9783540231851
Publisher:	Springer Berlin Heidelberg
Publication date:	12/22/2004
Series:	Lecture Notes in Control and Information Sciences , #310
Edition description:	2005
Pages:	199
Product dimensions:	6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

Introduction.- Neural network Wiener models.- Neural network Hammerstein models.- Polynomial Wiener models.- Polynomial Hammerstein models.- Applications.