Neural Network Systems Techniques and Applications: Advances In Theory And Applications by Cornelius T. LeondesNeural Network Systems Techniques and Applications: Advances In Theory And Applications by Cornelius T. Leondes

Neural Network Systems Techniques and Applications: Advances In Theory And Applications

byCornelius T. LeondesEditorLeondes

Hardcover | December 11, 1997

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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 Systemscovers 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.


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

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 adju...

Details & Specs

Title:Neural Network Systems Techniques and Applications: Advances In Theory And ApplicationsFormat:HardcoverDimensions:438 pages, 9 × 6 × 0.98 inPublished:December 11, 1997Publisher:Academic PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0124438679

ISBN - 13:9780124438675

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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.

From Our Editors

This text outlines neural network structures for achieving practical and effective systems and provides many examples of such systems. Practitioners, researchers and students in industrial, manufacturing, electrical, mechanical, and production engineering will find this volume an indispensable reference. Control and Dynamic Systems covers the important topics of highly effective Orthogonal Activation Function Based Neural Network System Architecture, multilayer recurrent neural networks for synthesizing and implementing real-time linear control and adaptive control of unknown nonlinear dynamical systems.