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Beschreibung
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.
A Block-Oriented Approach
Details
| Verlag | Springer Berlin |
| Ersterscheinung | 18. November 2004 |
| Maße | 23.5 cm x 15.5 cm |
| Gewicht | 341 Gramm |
| Format | Softcover |
| ISBN-13 | 9783540231851 |
| Auflage | 2005 |
| Seiten | 199 |
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