Identification of nonlinear systems using neural networks and polynomial models : a block-oriented approach
著者
書誌事項
Identification of nonlinear systems using neural networks and polynomial models : a block-oriented approach
（Lecture notes in control and information sciences, 310）
Springer, c2005
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注記
Includes bibliographical references (p. [187]-194) and index
内容説明・目次
内容説明
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.
目次
Introduction.- Neural network Wiener models.- Neural network Hammerstein models.- Polynomial Wiener models.- Polynomial Hammerstein models.- Applications.
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