Neural Systems for Control


  • Omid Omidvar, University of the District of Columbia
  • David Elliott, Institute of Systems Research, University of Maryland

Control problems offer an industrially important application and a guide to understanding control systems for those working in Neural Networks. Neural Systems for Control represents the most up-to-date developments in the rapidly growing aplication area of neural networks and focuses on research in natural and artifical neural systems directly applicable to control or making use of modern control theory. The book covers such important new developments in control systems such as intelligent sensors in semiconductor wafer manufacturing; the relation between muscles and cerebral neurons in speech recognition; online compensation of reconfigurable control for spacecraft aircraft and other systems; applications to rolling mills, robotics and process control; the usage of past output data to identify nonlinear systems by neural networks; neural approximate optimal control; model-free nonlinear control; and neural control based on a regulation of physiological investigation/blood pressure control. All researchers and students dealing with control systems will find the fascinating Neural Systems for Control of immense interest and assistance.
View full description


Researchers and practicioners in the fields of neural networks, control engineering, mechanical engineering, and electrical engineering.


Book information

  • Published: February 1997
  • ISBN: 978-0-12-526430-3

Table of Contents

Introduction: Neural Networks and Automatic Control. Reinforcement Learning. Neurocontrol in Sequence Recognition. A Learning Sensorimotor Map of Arm Movements: A Step Toward Biological Arm Control. Neuronal Modeling of the Baroceptor Reflex with Applications in Process Modeling and Control. Identification of Nonlinear Dynamical Systems Using Neural Networks. Neural Network Control of Robot Arms and Nonlinear Systems. Neual Networks for Intelligent Sensors and Control-PracticalIssues and Some Solutions. Approximation of Time-Optimal Control for an Industrial Production Plant with General Regression Neural Network. Neuro-Control Design: Reconfigurable Neural Control in Precision Space Structural Platforms. Neural Approximationsfor Finite- and Infinite-Horizon Optimal Control. Index.