%0 Journal Article
%T On the prediction of chaotic time series using a new generalized radial basis function neural networks<br>一种新型广义RBF神经网络在混沌时间序列预测中的研究
%A Li Jun
%A Liu Jun-Hua
%A <br>李军
%A 刘君华
%J 物理学报
%D 2005
%I 
%X Radial basis function (RBF) networks have been widely used for function approximation and pattern classification as an alternative to conventional feedforward neural networks. A novel generalized RBF neural network model is presented. The form of RBF is determined by a generator function, and then an easily implementable gradient decent learning algorithm for training the new generalized RBF networks is given. Simultaneously, a fast dynamic learning algorithm based on Kalman filter is also proposed to improve the performance and accelerate the convergence speed of the new generalized RBF networks. The generalized RBF neural networks based on Kalman filtering dynamic learning algorithm is then applied to the chaotic time series prediction on the Mackey-Glass equation and the Henon map to test the validity of this proposed model. Simulation results show that the new generalized RBF networks can accurately predict chaotic time series. It provides an attractive approach to study the properties of complex nonlinear system model and chaotic time series.
%K generalized radial basis function neural networks
%K Kalman filter
%K gradient decent learning algorithm
%K chaotic time series prediction<br>广义径向基函数神经网络，卡尔曼滤波，梯度下降学习算法，混沌时间序列，预测
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=6E709DC38FA1D09A4B578DD0906875B5B44D4D294832BB8E&cid=47EA7CFDDEBB28E0&jid=29DF2CB55EF687E7EFA80DFD4B978260&aid=660F244CC13D3253&yid=2DD7160C83D0ACED&vid=318E4CC20AED4940&iid=F3090AE9B60B7ED1&sid=7117302B60956AA0&eid=04793F78B243B054&journal_id=1000-3290&journal_name=物理学报&referenced_num=0&reference_num=18