Neural Network Systems Techniques and Applications: Advances in Theory and Applications
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Table of contents
- Cover
- Contentsv
- Contributorsxiii
- Prefacexv
- Chapter 1. Orthogonal Functions for Systems Identification and Control1
- I. Introduction1
- II. Neural Networks with Orthogonal Activation Functions2
- III. Frequency Domain Applications Using Fourier Series Neural Networks25
- IV. Time Domain Applications for System Identification and Control47
- V. Summary71
- References72
- Chapter 2. Multilayer Recurrent Neural Networks for Synthesizing and Tuning Linear Control Systems v75
- I. Introduction76
- II. Background Information77
- III. Problem Formulation79
- IV. Neural Networks for Controller Synthesis85
- V. Neural Networks for Observer Synthesis93
- VI. Illustrative Examples98
- VII. Concluding Remarks123
- References125
- Chapter 3. Direct and Indirect Techniques to Control Unknown Nonlinear Dynamical Systems Using Dynam127
- I. Introduction127
- II. Problem Statement and the Dynamic Neural Network Model130
- III. Indirect Control132
- IV. Direct Control139
- V. Conclusions154
- References154
- Chapter 4. A Receding Horizon Optimal Tracking Neurocontroller for Nonlinear Dynamic Systems157
- I. Introduction158
- II. Receding Horizon Optimal Tracking Control Problem Formulation159
- III. Design of Neurocontrollers163
- IV. Case Studies176
- V. Conclusions187
- References188
- Chapter 5. On-Line Approximators for Nonlinear System Identification: A Unified Approach191
- I. Introduction191
- II. Network Approximators193
- III. Learning Algorithm200
- IV Continuous-Time Identification210
- V Conclusions228
- References229
- Chapter 6. The Determination of Multivariable Nonlinear Models for Dynamic Systems231
- I. Introduction231
- II. The Nonlinear System Representation233
- III. The Conventional NARMAX Methodology235
- IV Neural Network Models246
- V Nonlinear-in-the-Parameters Approach254
- VI Linear-in-the-Parameters Approach259
- VII. Identifiability and Local Model Fitting271
- VIII. Conclusions273
- References275
- Chapter 7. High-Order Neural Network Systems in the Identification of Dynamical Systems279
- I. Introduction279
- II. RHONNs and g-RHONNs281
- III. Approximation and Stability Properties of RHONNs and g-RHONNs284
- IV. Convergent Learning Laws289
- V. The Boltzmann g-RHONN294
- VI. Other Applications298
- VII. Conclusions304
- References304
- Chapter 8. Neurocontrols for Systems with Unknown Dynamics307
- I. Introduction307
- II. The Test Cases309
- III. The Design Procedure313
- IV. More Details on the Controller Design318
- V. More on Performance320
- VI. Closure331
- References331
- Chapter 9. On-Line Learning Neural Networks for Aircraft Autopilot and Command Augmentation Systems333
- I. Introduction333
- II. The Neural Network Algorithms336
- III. Aircraft Model341
- IV. Neural Network Autopilots342
- V. Neural Network Command Augmentation Systems353
- VI. Conclusions and Recommendations for Additional Research379
- References380
- Chapter 10. Nonlinear System Modeling383
- I. Introduction383
- II. RBF Neural Network-Based Nonlinear Modeling385
- III. On-Line RBF Structural Adaptive Modeling394
- IV. Multiscale RBF Modeling Technique399
- V. Neural State–Space–Based Modeling Techniques406
- VI. Dynamic Back-Propagation409
- VII. Properties and Relevant Issues in State–Space Neural Modeling412
- VIII. Illustrative Examples419
- References431
- Index435
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 Systems covers 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.
Key Features
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
Control and Dynamic Systems covers 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.
Key Features
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