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Identification and Adaptive Control of Nonlinear Processes Using Combined Neural Networks and Genetic Algorithms

  • A. H. Abu-Alola
  • N. E. Gough
Conference paper

Abstract

This paper presents a new combined neural-genetic method for identifying processes comprising static nonlinearities and linear dynamics, and for adapting the model to linear and nonlinear parameter changes. The approach is based on multilayer and recurrent neural networks. It is shown that the powerful properties of genetic algorithms may be used to advantage in a learning neural network. We solve a number of problems and compare the results and the method with those previously reported by Narendra & Parthasarathy.

Keywords

Genetic Algorithm Hide Layer Recurrent Neural Network Considerable Research Effort Learning Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    Narendra, K.S. & Parthasarathy, K. Identification and control of dynamical systems using neural networks, IEEE Trans. on Neural Networks, 1, 1, 4–27 (1990).CrossRefGoogle Scholar
  2. [2]
    Goldberg, D.E. Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Publishing Co. (1989).MATHGoogle Scholar

Copyright information

© Springer-Verlag/Wien 1995

Authors and Affiliations

  • A. H. Abu-Alola
    • 1
  • N. E. Gough
    • 1
  1. 1.School of Computing and Information TechnologyUniversity of WolverhamptonWolverhamptonUK

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