Neural Adaptive Control Technology

By: Rafal Zbikowski (Editor), Kenneth J Hunt (Editor)

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Hardcover | 1 December 1995

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356 Pages

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This book is an outgrowth of the workshop on neural adaptive control technology, NAC1, held in 1995 in Glasgow. Selected workshop participants were asked to substantially expand and revise their contributions to make them into full papers. The workshop was organized in connection with a three-year European Union funded basic research project in the ESPRIT framework, called NACT, a collaboration between Daimler-Benz (Germany) and the University of Glasgow (Scotland). A major aim of the NACT project is to develop a systematic engineering procedure for designing neural controllers for nonlinear dynamic systems. The techniques developed are being evaluated on concrete industrial problems from Daimler-Benz. In the book emphasis is put on development of sound theory of neural adaptive control for nonlinear control systems, but firmly anchored in the engineering context of industrial practice. Therefore the contributors are both renowned academics and practitioners from major industrial users of neurocontrol.

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Non-Fiction Mathematics Calculus & Mathematical Analysis Engineering & Technology Technology in General Engineering in General Computing & I.T.Computer Science Artificial Intelligence Neural Networks & Fuzzy Systems Electronics & Communications Engineering Electronics Engineering

Automatic Control Engineering Mechanical Engineering & Materials Mechanical Engineering Booktopia Publisher Services World Scientific Publishing

Preface
Discrete-Time Neural Model Structures for Continuous Nonlinear Systems: Fundamental Properties and Control Aspects	p. 3
Continuous-Time Local Model Networks	p. 41
Nonuniform Sampling Approach to Control Systems Modelling with Feedforward Neural Networks	p. 71
Geometric Methods in Nonlinear Control Theory: A Survey	p. 115
Local Reachability, Local Controllability and Observability of a Class of 2-D Bilinear Systems	p. 153
Stable Adaptive Control of a General Class of Non-Linear Systems	p. 177
Robust Adaptive Neurocontrol of MIMO Continuous-Time Processes Based on the e[subscript 1]-Modification Scheme	p. 207
Black-Box Modeling with State-Space Neural Networks	p. 237
An Approach to Intelligent Identification and Control of Nonlinear Dynamical Systems	p. 265
How to Adapt in Neurocontrol: A Decision for CMAC	p. 285
The Equivalence of Spline Models and Fuzzy Logic Applied to Model Construction and Interpretation	p. 315
Index	p. 341
Table of Contents provided by Blackwell. All Rights Reserved.

Neural Adaptive Control Technology

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