Self-learning Fuzzy Controllers Based On a Real-time Reinforcement Genetic Algorithm  

作　　者：方建安 苗清影 郭钊侠 邵世煌 

出　　处：《Journal of Donghua University(English Edition)》2002年第2期19-22,共4页东华大学学报（英文版）

基　　金：theYouthTeacherProgramFoundationoftheStateEducationDepartmentandtheShanghaiShuguangProgramFoudation

摘　　要：This paper presents a novel method for constructing fuzzy controllers based on a real time reinforcement genetic algorithm. This methodology introduces the real-time learning capability of neural networks into globally searching process of genetic algorithm, aiming to enhance the convergence rate and real-time learning ability of genetic algorithm, which is then used to construct fuzzy controllers for complex dynamic systems without any knowledge about system dynamics and prior control experience. The cart-pole system is employed as a test bed to demonstrate the effectiveness of the proposed control scheme, and the robustness of the acquired fuzzy controller with comparable result.This paper presents a novel method for constructing fuzzy controllers based on a real time reinforcement genetic algorithm. This methodology introduces the real-time learning capability of neural networks into globally searching process of genetic algorithm, aiming to enhance the convergence rate and real-time learning ability of genetic algorithm, which is then used to construct fuzzy controllers for complex dynamic systems without any knowledge about system dynamics and prior control experience. The cart-pole system is employed as a test bed to demonstrate the effectiveness of the proposed control scheme, and the robustness of the acquired fuzzy controller with comparable result.

关 键 词：fuzzy controller  self-learning  REAL time reinforcement  GENETIC algorithm 

分 类 号：N[自然科学总论]