Chaos identification based on CMAC with replacing eligibility learning  

作　　者：SUN Yan-zhong 

机构地区：[1]Information and Electrical Engineering School, University of Panzhihua, Panzhihua 617000, P. R. China

出　　处：《重庆邮电大学学报（自然科学版）》2009年第2期300-304,共5页Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition)

摘　　要：In the conventional CMAC learning scheme, the correcting amounts of errors are equally distributed into all addressed weight, regardless the temporal credibility of those weights. In order to solve the temporal credit assignment problem of the CMAC, an improved CMAC neural network based on replacing eligibility learning concept was designed. The proposed improved leaning approach uses the replacing eligibility learning concept of the reinforcement learning to improve the prediction capability. The simulations for chaotic system identification show that the improved CMAC neural network is effective.In the conventional CMAC learning scheme, the correcting amounts of errors are equally distributed into all ad- dressed weight, regardless the temporal credibility of those weights. In order to solve the temporal credit assignment problem of the CMAC, an improved CMAC neural network based on replacing eligibility learning concept was designed. The proposed improved leaning approach uses the replacing eligibility learning concept of the reinforcement learning to improve the prediction capability. The simulations for chaotic system identification show that the improved CMAC neural network is effective.

关 键 词：CMAC神经网络 学习计划 资格 混沌识别 分配问题 概念设计 强化学习 学习方法 

分 类 号：O322[理学—一般力学与力学基础] TP18[理学—力学]