| Description |
1 online resource (xiii, 358 pages) : illustrations |
| 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 |
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Introduction : neural networks and automatic control / David L. Elliott -- Reinforcement learning / Andrew G. Barto -- Neurocontrol in sequence recognition / William J. Byrne and Shihab A. Shamma -- A learning sensorimotor map of arm movements : a step toward biological arm control / Sungzoon Cho, James A. Reggia and Min Jang -- Neuronal modeling of the baroreceptor reflex with applications in process modeling and control / Francis J. Doyle III [and others] -- Identification of nonlinear dynamical systems using neural networks / A.U. Levin and K.S. Narendra -- Neural network control of robot arms and nonlinear systems / F.L. Lweis, S. Jagannathan, and A. Yeşildirek -- Neural networks for intelligent sensors and control : practical issues and some solutions / S. Joe Qin -- Approximation of time-optimal control for an industrial production plant with general regression neural network / Clemens Schäffner and Dierk Schröder -- Neuro-control design : optimization aspects / H. Ted Su and Tariq Samad -- Reconfigurable neural control in precision space structural platforms / Gary G. Yen -- Neural approximations for finite- and infinite- horizon optimal control / Riccardo Zoppoli and Thomas Parisini |
| Summary |
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. Key Features * Focuses on research in natural and artifical neural systems directly applicable to contol or making use of modern control theory * Represents the most up-to-date developments in this rapidly growing application area of neural networks * Takes a new and novel approach to system identification and synthesis |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Neural networks (Computer science)
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Automatic control.
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Neural Networks, Computer
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TECHNOLOGY & ENGINEERING -- Automation.
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Automatic control
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Neural networks (Computer science)
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Neuronales Netz
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Regelungssystem
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Commande automatique.
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| Form |
Electronic book
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| Author |
Omidvar, Omid.
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Elliott, David L. (David LeRoy), 1932-
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| ISBN |
9780125264303 |
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0125264305 |
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9780080537399 |
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0080537391 |
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1281038458 |
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9781281038456 |
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