Evolutionary Learning Algorithms for Neural Adaptive Control

✍ Scribed by Dimitris C. Dracopoulos BSc, MSc, PhD, DIC (auth.)

Publisher: Springer-Verlag London
Year: 1997
Tongue: English
Leaves: 228
Series: Perspectives in Neural Computing
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Evolutionary Learning Algorithms for Neural Adaptive Control is an advanced textbook, which investigates how neural networks and genetic algorithms can be applied to difficult adaptive control problems which conventional results are either unable to solve , or for which they can not provide satisfactory results. It focuses on the principles involved, rather than on the modelling of the applications themselves, and therefore provides the reader with a good introduction to the fundamental issues involved.

✦ Table of Contents

Front Matter....Pages i-xi
Introduction....Pages 1-4
Dynamic Systems and Control....Pages 5-21
The Attitude Control Problem....Pages 23-46
Artificial Neural Networks....Pages 47-70
Neuromodels of Dynamic Systems....Pages 71-96
Current Neurocontrol Techniques....Pages 97-109
Genetic Algorithms....Pages 111-131
Adaptive Control Architecture....Pages 133-163
Conclusions and the Future....Pages 165-167
Back Matter....Pages 169-211

✦ Subjects

Artificial Intelligence (incl. Robotics); Complexity

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