Identification of Nonlinear Systems Using Neural Networks and Polynomial Models: A Block-Oriented Approach

By Andrzej Janczak

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.

• Format: Paperback
• Language: English
• ISBN: 3540231854
• ISBN13: 9783540231851
• Release Date: November 2004
• Publisher: Springer
• Length: 199 Pages
• Weight: 1.15 lbs.
• Dimensions: 0.5" x 8.5" x 11.0"