Neural Systems for Control
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Table of contents
- Cover
- Contentsv
- Contributorsix
- Prefacexiii
- Chapter 1. Introduction: Neural Networks and Automatic Control1
- 1 Control Systems1
- 2 What is a Neural Network?3
- Chapter 2. Reinforcement Learning7
- 1 Introduction7
- 2 Nonassociative Reinforcement Learning8
- 3 Associative Reinforcement Learning12
- 4 Sequential Reinforcement Learning20
- 5 Conclusion26
- 6 References27
- Chapter 3. Neurocontrol in Sequence Recognition31
- 1 Introduction31
- 2 HMM Source Models32
- 3 Recognition: Finding the Best Hidden Sequence33
- 4 Controlled Sequence Recognition34
- 5 A Sequential Event Dynamic Neural Network42
- 6 Neurocontrol in Sequence Recognition49
- 7 Observations and Speculations52
- 8 References56
- Chapter 4. A Learning Sensorimotor Map of Arm Movements: a Step Toward Biological Arm Control61
- 1 Introduction61
- 2 Methods63
- 3 Simulation Results70
- 4 Discussion82
- 5 References83
- Chapter 5. Neuronal Modeling of the Baroreceptor Reflex with Applications in Process Modeling and Co87
- 1 Motivation87
- 2 The Baroreceptor Vagal Reflex89
- 3 A Neuronal Model of the Baroreflex93
- 4 Parallel Control Structures in the Baroreflex102
- 5 Neural Computational Mechanisms for Process Modeling116
- 6 Conclusions and Future Work121
- 7 References122
- Chapter 6. Identification of Nonlinear Dynamical Systems Using Neural Networks129
- 1 Introduction129
- 2 Mathematical Preliminaries131
- 3 State space models for identification139
- 4 Identification Using Input–Output Models142
- 5 Conclusion154
- 6 Appendix: Proof of Lemma 1156
- 7 References158
- Chapter 7. Neural Network Control of Robot Arms and Nonlinear Systems161
- 1 Introduction161
- 2 Background in Neural Networks, Stability, and Passivity163
- 3 Dynamics of Rigid Robot Arms167
- 4 NN Controller for Robot Arms169
- 5 Passivity and Structure Properties of the NN183
- 6 Neural Networks for Control of Nonlinear Systems187
- 7 Neural Network Control with Discrete-Time Tuning193
- 8 Conclusion207
- 9 References207
- Chapter 8. Neural Networks for Intelligent Sensors and Control „ Practical Issues and Some Solutio213
- 1 Introduction213
- 2 Characteristics of Process Data215
- 3 Data Preprocessing217
- 4 Variable Selection220
- 5 Effect of Collinearity on Neural Network Training222
- 6 Integrating Neural Nets with Statistical Approaches225
- 7 Application to a Refinery Process230
- 8 Conclusions and Recommendations230
- 9 References231
- Chapter 9. Approximation of Time-Optimal Control for an Industrial Production Plant with General Reg235
- 1 Introduction235
- 2 Description of the Plant236
- 3 Model of the Induction Motor Drive238
- 4 General Regression Neural Network239
- 5 Control Concept242
- 6 Conclusion257
- 7 References257
- Chapter 10. Neuro-Control Design: Optimization Aspects259
- 1 Introduction259
- 2 Neuro-Control Systems260
- 3 Optimization Aspects273
- 4 PNC Design and Evolutionary Algorithm279
- 5 Conclusions281
- 6 References283
- Chapter 11. Reconfigurable Neural Control in Precision Space Structural Platforms289
- 1 Connectionist Learning System289
- 2 Reconfigurable Control293
- 3 Adaptive Time-Delay Radial Basis Function Network295
- 4 Eigenstructure Bidirectional Associative Memory297
- 5 Fault Detection and Identification302
- 6 Simulation Studies304
- 7 Conclusion309
- 8 References312
- Chapter 12. Neural Approximations for Finite- and Infinite-Horizon Optimal Control317
- 1 Introduction317
- 2 Statement of the Finite-Horizon Optimal Control Problem320
- 3 Reduction of Problem 1 to a Nonlinear Programming Problem321
- 4 Approximating Properties of the Neural Control Law323
- 5 Solution of Problem 2 by the Gradient Method327
- 6 Simulation Results330
- 7 The Infinite-Horizon Optimal Control Problem and Its Receding-Horizon Approximation335
- 8 Stabilizing Properties of the Receding-Horizon Regulator337
- 9 Neural Approximation for the Receding-Horizon Regulator340
- 10 Gradient Algorithm for Deriving the RH Neural Regulator; Simulation Results344
- 11 Conclusions348
- 12 References348
- Index353
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
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