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Neurocontrollers designed by a genetic algorithm

Hauessler, A., Li, Y., Ng, K.C., Murray-Smith, D.J., and Sharman, K.C. (1995) Neurocontrollers designed by a genetic algorithm. In: Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA 95), 12-14 Sep 1995, Sheffield, UK.

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Publisher's URL: http://dx.doi.org/10.1049/cp:19951104

Abstract

This paper first discusses problems existing in neural network design using mathematically guided training methods. It then presents a genetic algorithm based design technique to train the network, which overcomes all these problems. The paper also presents suitability conditions for using the genetic algorithm based design methods and develops, under these conditions, direct neurocontrollers with a novel structure inspired by proportional plus derivative control. Techniques are also developed to select the architectures in the same process of parameter training. The proposed methods are validated by several examples, including one with plant transport delay.

Item Type:Conference Proceedings
Additional Information:IEEE Conference Publication No. 414
Keywords:Artificial neural network, genetic algorithm, automatic control, proportional plus derivative control.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Murray-Smith, Prof David and Li, Prof Yun
Authors: Hauessler, A., Li, Y., Ng, K.C., Murray-Smith, D.J., and Sharman, K.C.
Subjects:Q Science > QA Mathematics > QA76 Computer software
T Technology > TJ Mechanical engineering and machinery
T Technology > TK Electrical engineering. Electronics Nuclear engineering
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
College of Science and Engineering > School of Engineering

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