A study of Chaos Identification Based on CMAC with Replacing Eligibility Learning

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 is designed. The proposed improved leaning approach uses the replacing eligibility learning concept of the reinforcement learning to improve the prediction capability. The simulations on chaotic system identification get good result, which shows that the improved CMAC neural network is possible and effective.

关键词： CMAC Replace eligibility learning Chaos identification

作者: Yanzhong Sun

作者单位: Information and Electrical Engineering School University of Panzhihua Panzhihua,Sichuan 617, China

会议类型: 国际会议

会议名称: 2008 Sino-European Workshop on Intelligent Robots and Systems(SEIROS08)(第一届中欧智能系统及机器人国际学术研讨会)

会议地点: 重庆

会议语种:英文

页码: 1-4

在线出版日期: 2008-12-11（万方平台首次上网日期，不代表论文的发表时间）