Neural Computing & Applications

, Volume 6, Issue 1, pp 12–18

Genetic design of real-time neural network controllers

Articles

DOI: 10.1007/BF01670149

Cite this article as:
Hunter, A., Hare, G. & Brown, K. Neural Comput & Applic (1997) 6: 12. doi: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.

Keywords

Flow systems Genetic algorithms Neural networks Real-time control Reinforcement learning 

Copyright information

© Springer-Verlag London Limited 1997

Authors and Affiliations

  1. 1.Department of Computing and Information SystemsUniversity of SunderlandSunderlandUK
  2. 2.Entec UK Ltd.Northumbria House, Manor WalksCramlingtonUK

Personalised recommendations