A MULTILAYERED NEURAL-NET CONTROLLER USING DIRECT LEARNING ALGORITHM

This paper describes a recent study on the application of neural networks to dynamic system control. We consider two learning algorithms: the error backpropagation algorithm and the parallel recursive prediction error algorithm. The proposed method is an on-line approach of a multilayered neural network controller which does not require any information about the system dynamics. Lengthy training of the controller might be eliminated using the proposed approach. The implementation aspects of the proposed approaches to dynamic system control are discussed by case studies.