ENGLISH

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

By Andrzej Janczak (auth.)

Book information

Publisher
Springer-Verlag Berlin Heidelberg
Year
2005
ISBN
978-3-540-23185-1, 978-3-540-31596-4
DOI
10.1007/b98334
Language
english
Format
PDF
Filesize
5 MB (4805702 bytes)
Series
Lecture Notes in Control and Information Science 310
Edition
1
Pages
199\207
Orientation
yes
Scanned
yes
Time added
2013-08-01 04:00:00
Control Engineering; VibrationControl; ComplexityControl; Systems TheoryDynamical Systems

Description

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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