差分RBF神经网络的预测算法及其应用
A PREDICTIVE ALGORITHM BASED ON DIFFERENCE RADIAL BASIS FUNCTION NEURAL NETWORKS MODEL FOR TIME SERIES AND ITS APPLICATION
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摘要: 针对RBF（Radial Basis Function）神经网络处理非平稳时间序列的不足，本文提出一种修正的差分RBF神经网络结构，并给出相应的预测算法，将其应用于金融领域，对上证指数进行预测，结果表明其性能优于传统的RBF网络．Abstract: Because the performance of the classical radial basis function(RBF) predictor for non-stationary time series is less satisfactory, a modified structure called Difference RBF(DRBF) and its predictive algorithm are presented. Through the application to the prediction of the Shanghai stock index, the simulation results confirm the superior performance of the DRBF over the classical RBF, and the former is more fit for the non stationary time series problems.
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