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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
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