Overview
- First book on neural network and polynomial approach to identification of Wiener and Hammerstein systems.
- Includes supplementary material: sn.pub/extras
Part of the book series: Lecture Notes in Control and Information Sciences (LNCIS, volume 310)
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Table of contents (6 chapters)
Keywords
About this book
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.
Bibliographic Information
Book Title: Identification of Nonlinear Systems Using Neural Networks and Polynomial Models
Book Subtitle: A Block-Oriented Approach
Authors: Andrzej Janczak
Series Title: Lecture Notes in Control and Information Sciences
DOI: https://doi.org/10.1007/b98334
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2005
Softcover ISBN: 978-3-540-23185-1Published: 18 November 2004
eBook ISBN: 978-3-540-31596-4Published: 12 November 2004
Series ISSN: 0170-8643
Series E-ISSN: 1610-7411
Edition Number: 1
Number of Pages: XIV, 199
Topics: Control, Robotics, Mechatronics, Vibration, Dynamical Systems, Control, Systems Theory, Control, Complex Systems, Statistical Physics and Dynamical Systems