Neural Network Systems Techniques and Applications: Advances in Theory and Applications / Edition 1

by Cornelius T. Leondes

Overview

The book emphasizes neural network structures for achieving practical and effective systems, and provides many examples. Practitioners, researchers, and students in industrial, manufacturing, electrical, mechanical,and production engineering will find this volume a unique and comprehensive reference source for diverse application methodologies.

Control and Dynamic Systems covers the important topics of highly effective Orthogonal Activation Function Based Neural Network System Architecture, multi-layer recurrent neural networks for synthesizing and implementing real-time linear control,adaptive control of unknown nonlinear dynamical systems, Optimal Tracking Neural Controller techniques, a consideration of unified approximation theory and applications, techniques for the determination of multi-variable nonlinear model structures for dynamic systems with a detailed treatment of relevant system model input determination, High Order Neural Networks and Recurrent High Order Neural Networks, High Order Moment Neural Array Systems, Online Learning Neural Network controllers, and Radial Bias Function techniques.

Product Details

ISBN-13:	9780124438675
Publisher:	Elsevier Science
Publication date:	12/11/1997
Series:	Control and Dynamic Systems , #7
Pages:	438
Product dimensions:	6.00(w) x 9.00(h) x (d)

About the Author

Cornelius T. Leondes received his B.S., M.S., and Ph.D. from the University of Pennsylvania and has held numerous positions in industrial and academic institutions. He is currently a Professor Emeritus at the University of California, Los Angeles. He has also served as the Boeing Professor at the University of Washington and as an adjunct professor at the University of California, San Diego. He is the author, editor, or co-author of more than 100 textbooks and handbooks and has published more than 200 technical papers. In addition, he has been a Guggenheim Fellow, Fulbright Research Scholar, IEEE Fellow, and a recipient of IEEE's Baker Prize Award and Barry Carlton Award.

Table of Contents

Zhu, Shukl, and Paul, Orthogonal Functions for Systems Identification and Control. Wang, Multilayer Recurrent Neural Networks for Synthesizing and Tuning Linear Control Systems via Pole Assignment. Rovithakis andChristodoulou, Direct and Indirect Techniques to Control Unknown Nonlinear Dynamical Systems Using Dynamical Neural Networks. Park, Choi, and Lee, A Receding Horizon Optimal Tracking Neuro-Controller for Nonlinear Dynamic Systems. Polycarpou, On-Line Approximators for Nonlinear System Identification: A Unified Approach. Billings and Chen, The Determination of Multivariable Nonlinear Models for Dynamic Systems. Kosmatopoulos and Christodoulou, High-Order Neural Network Systems in the Identification of Dynamical Systems. Porter, Liu, and Trevino, Neurocontrols for Systems with Unknown Dynamics. Napolitano and Kincheloe, On-Line Learning Neural Networks for Aircraft Autopilot and Command Augmentation Systems. Tan, Suykens, Yu, and Vandewalle, Nonlinear System Modeling.

Editorial Reviews

Examines the effectiveness and essential significance of the techniques available and (with further development) the role they will play in the future. Ten chapters cover topics such as orthogonal activation function based neural network system architecture, multilayer recurrent neural networks for synthesizing and implementing real-time linear control, adaptive control of unknown nonlinear dynamical systems, optimal tracking neural controller techniques, unified approximation theory and applications, and techniques for determining multivariable nonlinear model structures for dynamic systems with a detailed treatment of relevant system model input determination. Annotation c. by Book News, Inc., Portland, Or.

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