%0 Journal Article
%T Chaos control of ferroresonance system based on improved RBF neural network<br>基于改进的径向基函数神经网络的铁磁谐振系统混沌控制
%A Sima Wen-Xia
%A Liu Fan
%A Sun Cai-Xin
%A Liao Rui-Jin
%A Yang Qing
%A <br>司马文霞
%A 刘凡
%A 孙才新
%A 廖瑞金
%A 杨庆
%J 物理学报
%D 2006
%I 
%X Facing to the ferroresonance over voltage of neutral grounded power system, an improved learning algorithm based on RBF neural networks is used to control the chaos system. The algorithm optimizes the object function to derive learning rule of central vectors, and uses the clustering function of network hidden layers.It improves the regression and learning ability of neural networks. The academic derivation testifies the errors and precision could satisfy demand of chaos control.And simulation calculation also displayed that the rate of convergence of the improved RBF neural network is much quickly and approach ability is much stronger. The numerical experimentation of ferroresonance system testifies the reliability and stability of using the algorithm to control chaos in neutral grounded power system.
%K neutral grounded power system
%K chaos control
%K radial basis function
%K maximum-entropy principle<br>中性点直接接地系统
%K 混沌控制
%K 径向基函数
%K 极大熵原理
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=6E709DC38FA1D09A4B578DD0906875B5B44D4D294832BB8E&cid=47EA7CFDDEBB28E0&jid=29DF2CB55EF687E7EFA80DFD4B978260&aid=FABA7AD637DF1182&yid=37904DC365DD7266&vid=E514EE58E0E50ECF&iid=708DD6B15D2464E8&sid=BAFB39DB22121EF6&eid=323EAC9BD716C39B&journal_id=1000-3290&journal_name=物理学报&referenced_num=0&reference_num=17