基于带回归权重RBF-AR模型的混沌时间序列预测

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。

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

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 th...