UID:
edocfu_9959235989402883
Umfang:
1 online resource (459 p.)
ISBN:
1-281-07077-7
,
9786611070779
,
0-08-055390-7
Serie:
Neural network systems, techniques, and applications ; v. 7
Inhalt:
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 contr
Anmerkung:
Description based upon print version of record.
,
Front Cover; Control and Dynamic Systems; Copyright Page; Contents; Contributors; Preface; Chapter 1. Orthogonal Functions for Systems Identification and Control; I. Introduction; II. Neural Networks with Orthogonal Activation Functions; III. Frequency Domain Applications Using Fourier Series Neural Networks; IV. Time Domain Applications for System Identification and Control; V. Summary; References; Chapter 2. Multilayer Recurrent Neural Networks for Synthesizing and Tuning Linear Control Systems via Pole Assignment; I. Introduction; II. Background Information; III. Problem Formulation
,
IV. Neural Networks for Controller SynthesisV. Neural Networks for Observer Synthesis; VI. Illustrative Examples; VII. Concluding Remarks; References; Chapter 3. Direct and Indirect Techniques to Control Unknown Nonlinear Dynamical Systems Using Dynamical Neural Networks; I. Introduction; II. Problem Statement and the Dynamic Neural Network Model; III. Indirect Control; IV. Direct Control; V. Conclusions; References; Chapter 4. A Receding Horizon Optimal Tracking Neurocontroller for Nonlinear Dynamic Systems; I. Introduction; II. Receding Horizon Optimal Tracking Control Problem Formulation
,
III. Design of NeurocontrollersIV. Case Studies; V. Conclusions; References; Chapter 5. On-Line Approximators for Nonlinear System Identification: A Unified Approach; I. Introduction; II. Network Approximators; III. Learning Algorithm; IV Continuous-Time Identification; V Conclusions; References; Chapter 6. The Determination of Multivariable Nonlinear Models for Dynamic Systems; I. Introduction; II. The Nonlinear System Representation; III. The Conventional NARMAX Methodology; IV Neural Network Models; V Nonlinear-in-the-Parameters Approach; VI Linear-in-the-Parameters Approach
,
VII. Identifiability and Local Model FittingVIII. Conclusions; References; Chapter 7. High-Order Neural Network Systems in the Identification of Dynamical Systems; I. Introduction; II. RHONNs and g-RHONNs; III. Approximation and Stability Properties of RHONNs and g-RHONNs; IV. Convergent Learning Laws; V. The Boltzmann g-RHONN; VI. Other Applications; VII. Conclusions; References; Chapter 8. Neurocontrols for Systems with Unknown Dynamics; I. Introduction; II. The Test Cases; III. The Design Procedure; IV. More Details on the Controller Design; V. More on Performance; VI. Closure; References
,
Chapter 9. On-Line Learning Neural Networks for Aircraft Autopilot and Command Augmentation SystemsI. Introduction; II. The Neural Network Algorithms; III. Aircraft Model; IV. Neural Network Autopilots; V. Neural Network Command Augmentation Systems; VI. Conclusions and Recommendations for Additional Research; References; Chapter 10. Nonlinear System Modeling; I. Introduction; II. RBF Neural Network-Based Nonlinear Modeling; III. On-Line RBF Structural Adaptive Modeling; IV. Multiscale RBF Modeling Technique; V. Neural State-Space-Based Modeling Techniques; VI. Dynamic Back-Propagation
,
VII. Properties and Relevant Issues in State-Space Neural Modeling
,
English
Weitere Ausg.:
ISBN 0-12-443867-9
Sprache:
Englisch
Bookmarklink
