Identification of Nonlinear Systems Using Neural Networks and Polynomial Models

Name: Identification of Nonlinear Systems Using Neural Networks and Polynomial Models
Brand: Springer Nature
SKU: 9783540315964
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Springer Nature

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9783540315964

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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.

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