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Identification Of Nonlinear Systems Using Neural Networks And Polynomial Models: A Block-Oriented Approach (Lecture Notes In Control And Information Sciences)

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.

Series: Lecture Notes in Control and Information Sciences (Book 310)

Paperback: 199 pages

Publisher: Springer; 2005 edition (February 22, 2009)

Language: English

ISBN-10: 3540231854

ISBN-13: 978-3540231851

Product Dimensions: 6.1 x 0.5 x 9.2 inches

Shipping Weight: 9.9 ounces (View shipping rates and policies)

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