Identification of Nonlinear Systems Using Neural Networks and Polynomial Models A Block-Oriented Approach
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 gi...
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| Format: | eBook |
| Language: | English |
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Berlin, Heidelberg
Springer Berlin Heidelberg
2005, 2005
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| Edition: | 1st ed. 2005 |
| Series: | Lecture Notes in Control and Information Sciences
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| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
| Summary: | 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 |
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| Physical Description: | XIV, 199 p online resource |
| ISBN: | 9783540315964 |