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Table of contents (6 chapters)
Keywords
About this book
The book presents both classical and novel network architectures and learning algorithms for modelling and control. Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems. A major contribution of this book is to introduce NLq Theory as an extension towards modern control theory, in order to analyze and synthesize non-linear systems that contain linear together with static non-linear operators that satisfy a sector condition: neural state space control systems are an example. Moreover, it turns out that NLq Theory is unifying with respect to many problems arising in neural networks, systems and control. Examples show that complex non-linear systems can be modelled and controlled within NLq theory, including mastering chaos.
The didactic flavor of this book makes it suitable for use as a text for a course on Neural Networks. In addition, researchers and designers will find many important new techniques, in particular NLqTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.
Authors and Affiliations
Bibliographic Information
Book Title: Artificial Neural Networks for Modelling and Control of Non-Linear Systems
Authors: Johan A. K. Suykens, Joos P. L. Vandewalle, Bart L. R. Moor
DOI: https://doi.org/10.1007/978-1-4757-2493-6
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag US 1996
Hardcover ISBN: 978-0-7923-9678-9Published: 31 December 1995
Softcover ISBN: 978-1-4419-5158-8Published: 07 December 2010
eBook ISBN: 978-1-4757-2493-6Published: 06 December 2012
Edition Number: 1
Number of Pages: XII, 235
Topics: Circuits and Systems, Complex Systems, Systems Theory, Control, Electrical Engineering, Statistical Physics and Dynamical Systems