Identification of Nonlinear Systems Using Neural Networks and Polynomial Models
A Block-Oriented Approach
(Sprache: Englisch)
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...
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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.
Inhaltsverzeichnis zu „Identification of Nonlinear Systems Using Neural Networks and Polynomial Models “
Introduction.- Neural network Wiener models.- Neural network Hammerstein models.- Polynomial Wiener models.- Polynomial Hammerstein models.- Applications.
Bibliographische Angaben
- Autor: Andrzej Janczak
- 2004, 2005, 199 Seiten, Maße: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer
- ISBN-10: 3540231854
- ISBN-13: 9783540231851
- Erscheinungsdatum: 18.11.2004
Sprache:
Englisch
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