基于带回归权重RBF-AR模型的混沌时间序列预测

Predicting chaotic time series using RBF-AR model with regression weight

提出了用带回归权重的径向基函数(radial basis function,RBF)网络来逼近状态相依自回归(autogressive,AR)模型中的函数系数,得到了带回归权重的RBF-AR模型。在这种模型中,RBF神经网络的输出权重已不是单一的常量,而是输入变量的线性回归函数。一种快速收敛的结构化非线性参数优化方法被用来估计提出的模型,辨识出的模型用来预测两组著名的混沌时间序列:Mackey-Glass时间序列和Lorenz吸引子时间序列。实验结果表明,提出的模型在预测精度上要优于其他一些现存的模型。

This paper proposes to use a new type of radial basis function（RBF） network to approximate the functional coefficients of the state-dependent autoregressive model.The output weights of the new type RBF network,instead of constant parameters normally used,are the linear regression functions of the input variables.A fast-converging estimation method is applied to optimize the parameters of the model.Two benchmark chaotic time series,Mackey-Glass time series and Lorenz attractor time series,are used to test the performance of the proposed model.Simulation tests show that the predictive accuracy of the model is much better than that of other existing models.