Genetic design of real-time neural network controllers

Hunter, Andrew and Hare, G. and Brown, K. (1997) Genetic design of real-time neural network controllers. Journal of Neural Computing and Applications, 6 (1). pp. 12-18. ISSN 0941-0643

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Full text URL: http://dx.doi.org/10.1007/BF01670149

Abstract

The use of genetic algorithms to design neural networks for real-time control of flows in sewerage networks is discussed. In many control applications, standard supervised learning techniques (such as back-propagation) cannot be used through lack of training data. Reinforcement learning techniques, such as genetic algorithms, are a computationally-expensive but viable alternative if a simulator is available for the system in question. The paper briefly describes why genetic algorithms and neural networks were selected, then reports the results of a feasibility study. This demonstrates that the approach does indeed have merits. The implications of high computational cost are discussed, in terms of scaling up to significantly complex problems.

Item Type:Article
Additional Information:The use of genetic algorithms to design neural networks for real-time control of flows in sewerage networks is discussed. In many control applications, standard supervised learning techniques (such as back-propagation) cannot be used through lack of training data. Reinforcement learning techniques, such as genetic algorithms, are a computationally-expensive but viable alternative if a simulator is available for the system in question. The paper briefly describes why genetic algorithms and neural networks were selected, then reports the results of a feasibility study. This demonstrates that the approach does indeed have merits. The implications of high computational cost are discussed, in terms of scaling up to significantly complex problems.
Keywords:Flow systems, Genetic algorithms, Neural networks, Real-time control, Reinforcement learning
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:2780
Deposited By: Rosaline Smith
Deposited On:14 Jul 2010 13:22
Last Modified:18 Jul 2011 16:27

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