UID:
almafu_9959233132802883
Umfang:
1 online resource (375 p.)
ISBN:
1-281-03845-8
,
9786611038458
,
0-08-053739-1
Inhalt:
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 application area of neural networks and focuses on research in natural and artificial 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
Anmerkung:
Description based upon print version of record.
,
Front Cover; Neural Systems for Control; Copyright Page; Contents; Contributors; Preface; Chapter 1. Introduction: Neural Networks and Automatic Control; 1 Control Systems; 2 What is a Neural Network?; Chapter 2. Reinforcement Learning; 1 Introduction; 2 Nonassociative Reinforcement Learning; 3 Associative Reinforcement Learning; 4 Sequential Reinforcement Learning; 5 Conclusion; 6 References; Chapter 3. Neurocontrol in Sequence Recognition; 1 Introduction; 2 HMM Source Models; 3 Recognition: Finding the Best Hidden Sequence; 4 Controlled Sequence Recognition
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5 A Sequential Event Dynamic Neural Network; 6 Neurocontrol in Sequence Recognition; 7 Observations and Speculations; 8 References; Chapter 4. A Learning Sensorimotor Map of Arm Movements: a Step Toward Biological Arm Control; 1 Introduction; 2 Methods; 3 Simulation Results; 4 Discussion; 5 References; Chapter 5. Neuronal Modeling of the Baroreceptor Reflex with Applications in Process Modeling and Control; 1 Motivation; 2 The Baroreceptor Vagal Reflex; 3 A Neuronal Model of the Baroreflex; 4 Parallel Control Structures in the Baroreflex; 5 Neural Computational Mechanisms for Process Modeling
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6 Conclusions and Future Work; 7 References; Chapter 6. Identification of Nonlinear Dynamical Systems Using Neural Networks; 1 Introduction; 2 Mathematical Preliminaries; 3 State space models for identification; 4 Identification Using Input-Output Models; 5 Conclusion; 6 Appendix: Proof of Lemma 1; 7 References; Chapter 7. Neural Network Control of Robot Arms and Nonlinear Systems; 1 Introduction; 2 Background in Neural Networks, Stability, and Passivity; 3 Dynamics of Rigid Robot Arms; 4 NN Controller for Robot Arms; 5 Passivity and Structure Properties of the NN
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6 Neural Networks for Control of Nonlinear Systems; 7 Neural Network Control with Discrete-Time Tuning; 8 Conclusion; 9 References; Chapter 8. Neural Networks for Intelligent Sensors and Control - Practical Issues and Some Solutions; 1 Introduction; 2 Characteristics of Process Data; 3 Data Preprocessing; 4 Variable Selection; 5 Effect of Collinearity on Neural Network Training; 6 Integrating Neural Nets with Statistical Approaches; 7 Application to a Refinery Process; 8 Conclusions and Recommendations; 9 References
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Chapter 9. Approximation of Time-Optimal Control for an Industrial Production Plant with General Regression Neural Network; 1 Introduction; 2 Description of the Plant; 3 Model of the Induction Motor Drive; 4 General Regression Neural Network; 5 Control Concept; 6 Conclusion; 7 References; Chapter 10. Neuro-Control Design: Optimization Aspects; 1 Introduction; 2 Neuro-Control Systems; 3 Optimization Aspects; 4 PNC Design and Evolutionary Algorithm; 5 Conclusions; 6 References; Chapter 11. Reconfigurable Neural Control in Precision Space Structural Platforms; 1 Connectionist Learning System
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2 Reconfigurable Control
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English
Weitere Ausg.:
ISBN 0-12-526430-5
Sprache:
Englisch
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