- 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",
- isbn13 = "978-1-4471-0903-7",
-
URL = "
http://www.amazon.co.uk/exec/obidos/ASIN/3540761616/qid%3D1106423488/202-4979008-1846244",
-
URL = "http://www.springer.com/computer/ai/book/978-3-540-76161-7",
-
DOI = "
doi:10.1007/978-1-4471-0903-7",
- size = "222 pages",
-
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 \cite{Dracopoulos:1997:es}",
- size = "212 pages",
