【摘要】 给出了基于径向基函数网络的混沌时间序列预测的方法。利用非线性自回归移动平均（ＮＡＲＭＡＸ） 模型对非线性时间序列进行辨识并给出基于动态径向基函数（ＲＢＦ） 网络的辨识算法。将这一方法应用到Ｈｅｎｏｎ 映射的混沌时间序列的嵌入维估计及我国股票市场的混沌现象的实证研究，得到理想的结果。文章最后指出了进一步的研究方向。更多还原

【Abstract】 A method based on radial basis function networks for forecasting chaotic time series is proposed.The nonlinear time series identification problem is formulated with a nonlinear autoregressive moving average(NARMAX)model then a new identification algorithm based on dynamic radial basis function networks is proposed.Then this method is applied to the estimation of embedding dimension for chaotic time series of Henon mapping and the confirmation of the chaotic phenomena in stock markets of China,from which one can get the desired results.Further research directions are also pointed out.更多还原