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.
| ISBN: | 9783540231851 |
| Publication date: | 18th November 2004 |
| Author: | A Janczak |
| Publisher: | Springer an imprint of Springer Berlin Heidelberg |
| Format: | Paperback |
| Pagination: | 195 pages |
| Series: | Lecture Notes in Control and Information Sciences |
| Genres: |
Automatic control engineering Engineering: Mechanics of solids Cybernetics and systems theory Mathematical physics |
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 Polynomial Models features in the following genres: Automatic control engineering, Engineering: Mechanics of solids, Cybernetics and systems theory, Mathematical physics
Paperback. Not Available.
Identification of Nonlinear Systems Using Neural Networks and Polynomial Models was written by A Janczak and published by Springer an imprint of Springer Berlin Heidelberg
Identification of Nonlinear Systems Using Neural Networks and Polynomial Models has 195 pages
Yes it is part of Lecture Notes in Control and Information Sciences series