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所属单位：北京航空航天大学
发表刊物：IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
项目来源：北京市科技重点项目
关键字：Adaptive radial basis function neural network (RBFNN); dynamic model; residual approximate error; sm
摘要：This paper proposes an online learning adaptive radial basis function neural network (RBFNN) to deal with measurement errors and environment disturbances to improve control performance. Since the weight matrix of the adaptive neural network can be updated online by the state error information, the adaptive neural network can be constructed directly without prior training. Moreover, with the parameter optimization rule, the residual approximation error can be reduced by the maximum absolute position error, average position error, and mean square position error in sampling windows. The applicabi
合写作者： Pei,Lu, Xusheng,Lei
论文类型：基础研究
文献类型：期刊
是否译文：否
发表时间：2013-11-07
发布期刊链接：http://xueshu.baidu.com/s?wd=paperuri%3A%2810467395e451248da28ed9920a065fe8%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fieeexplore.ieee.org%2Fdocument%2F6657779%2F&ie=utf-8