Optimizing neural fuzzy system for robot control

Abstract

Along with the strong development of science and technology, fuzzy system and artificial neural network are increasingly widely applied in multiple field. Base on human logic, with advantages that is simple and it process exactly uncertain information; fuzzy system and artificial neural network have brought many considerable efficient in field of control and automation. Optimizing neural fuz^ system is important and necessary to improve control efficiency, creating fundamental factors to resolve technical problems with high accurate, especially problems that we can not modeling or difficult. This science article perform applications of fuzzy logic and fuzzy algebra for fuzzy control, incorporating artificial neural network and fuzzy algebra control, combining genetic algorithm and error back propagation algorithm to improve accurateness of the weights of artificial neural network.