Designing neural networks to improve timing performances of intelligent controllers

Abstract

Intelligent techniques are applied to improve the control methods of physical quantities. Many researchers employ the fuzzy logic combined with genetic procedures to achieve good control results. Such intelligent techniques can be enhanced using Neural Networks. In this paper, an optimization algorithm to define the best training set for suitable Neural Networks is designed. The network trained with the optimal sample is introduced into genetic-fuzzy controllers to improve the timing performances. The results show that the genetic-neuro-fuzzy controllers are better than genetic-fuzzy controllers in terms of settling time and rise time.