Evolutionary Learning Algorithms for Neural Adaptive Control   [EL] [AC]

by

Dracopoulos, D., C.

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Info: 1997
Keywords:genetic algorithms, genetic programming
Abstract:
Neural networks [NN] and evolutionary algorithms [EA] are constantly expanding their field of application to a variety of new domains. One area of particular interest is their applicability to control and adaptive control systems: [AC] [CS] the limitations of the classical control theory combined with the need for greater robustness, adaptivity and ``intelligence'' make neurocontrol and evolutionary control algorithms an attractive (and in some cases, the only) alternative. After an introduction to neural networks [NN] and genetic algorithms, [GA] this volume describes in detail how neural networks [NN] and evolutionary techniques (specifically genetic algorithms [GA] and genetic programming) [GP] can be applied to the adaptive control [AC] of complex dynamic systems [DS] (including chaotic ones). A number of examples are presented and useful tips are given for the application of the techniques described. The fundamentals of dynamic systems theory [DS] and classical adaptive control [AC] are also given.
Notes:
Chapter 7 deals with genetic algorithms, including 8 pages on genetic programming. These include solving the problem described in Dracopoulos:1997:es
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BibTex:
@Book{dracopoulos:1997:elanac,
  author =       "Dimitris C. Dracopoulos",
  title =        "Evolutionary Learning Algorithms for Neural Adaptive
                 Control",
  publisher =    "Springer Verlag",
  year =         "1997",
  series =       "Perspectives in Neural Computing",
  address =      "P.O. Box 31 13 40, D-10643 Berlin, Germany",
  month =        aug,
  email =        "orders@springer.de",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-76161-6",
  URL =          "http://www.springer.de/catalog/html-files/deutsch/comp/3540761616.html",
  abstract =     "Neural networks and evolutionary algorithms are
                 constantly expanding their field of application to a
                 variety of new domains. One area of particular interest
                 is their applicability to control and adaptive control
                 systems: the limitations of the classical control
                 theory combined with the need for greater robustness,
                 adaptivity and ``intelligence'' make neurocontrol and
                 evolutionary control algorithms an attractive (and in
                 some cases, the only) alternative.

                 After an introduction to neural networks and genetic
                 algorithms, this volume describes in detail how neural
                 networks and evolutionary techniques (specifically
                 genetic algorithms and genetic programming) can be
                 applied to the adaptive control of complex dynamic
                 systems (including chaotic ones). A number of examples
                 are presented and useful tips are given for the
                 application of the techniques described. The
                 fundamentals of dynamic systems theory and classical
                 adaptive control are also given.",
  notes =        "Chapter 7 deals with genetic algorithms, including 8
                 pages on genetic programming. These include solving the
                 problem described in Dracopoulos:1997:es",
  size =         "212 pages",
}