Evolutionary Learning Algorithms for Neural Adaptive Control

Evolutionary Learning Algorithms for Neural Adaptive Control by Dimitris C. Dracopoulos.

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 satisfac...

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Bibliographic Details
Main Author: Dracopoulos, Dimitris C. (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: London : Springer London : Imprint: Springer, 1997.
Edition:1st ed. 1997.
Series:Perspectives in Neural Computing,
Springer eBook Collection.
Subjects:
Artificial intelligence.
Computational complexity.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • Introduction
  • Dynamic systems and control
  • The attitude control problem
  • Artificial neural networks
  • Neuromodels of dynamic systems
  • Current neurocontrol techniques
  • Genetic algorithms
  • Adaptive control architectures
  • Conclusions and the future
  • A. Euler equations solutions
  • B. An attitude control simulator
  • Bibliography
  • Index.

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