Identification of Nonlinear Systems Using Neural Networks and Polynomial Models [electronic resource] : A Block-Oriented Approach / by Andrzej Janczak.

By:

Janczak, Andrzej [author.]

Contributor(s):

SpringerLink (Online service)

Material type: Text

TextSeries: Lecture Notes in Control and Information Sciences ; 310Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2005Edition: 1st ed. 2005Description: XIV, 199 p. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783540315964

Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:

629.8 23

LOC classification:

TJ210.2-211.495
TJ163.12

Online resources:

Click here to access online

Contents:

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

In: Springer eBooksSummary: 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.

Holdings ( 1 )
Title notes ( 2 )

Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
eBook	e-Library	EBook		Available

Total holds: 0

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

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.