Neural Network Systems Techniques and Applications
Advances in Theory and Applications
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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.
Key Features
Coverage includes:
- Orthogonal Activation Function Based Neural Network System Architecture (OAFNN)
- Multilayer recurrent neural networks for synthesizing and implementing real-time linear control
- Adaptive control of unknown nonlinear dynamical systems
- Optimal Tracking Neural Controller techniques
- Consideration of unified approximation theory and applications
- Techniques for determining multivariable nonlinear model structures for dynamic systems, with a detailed treatment of relevant system model input determination
Readership
Practitioners, research workers, academicians, and students in mechanical, electrical, industrial, manufacturing, and production engineering
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.
Product details
- No. of pages: 438
- Language: English
- Copyright: © Academic Press 1997
- Published: December 11, 1997
- Imprint: Academic Press
- eBook ISBN: 9780080553900
About the Serial Editor
Cornelius Leondes
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.
Affiliations and Expertise
University of California, Los Angeles, U.S.A.
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