基于一种新型聚类算法的RBF神经网络混沌时间序列预测

运用两阶段学习方法构建径向基函数(RBF)神经网络模型预测混沌时间序列.在利用非监督学习算法确定网络隐层中心时,提出了一种基于高斯基的距离度量,并联合输入输出聚类的策略.基于Fisher可分离率设计高斯基距离度量中的惩罚因子,可以提高聚类的性能.而输入输出聚类策略的引入,建立了聚类性能与网络预测性能之间的联系.因此,根据本文方法构建的网络模型,一方面可以加快网络训练的速度,另一方面可以提高预测性能.将该方法对Mackey-Glass, Lorenz和Logistic混沌时间序列进行了预测仿真研究,仿真结果表明了该方法的有效性. 关键词： 混沌时间序列 预测 径向基神经网络 聚类

Chaotic time series prediction based on RBF neural networks with a new clustering algorithm

Two-phase learning method is considered in this paper to configure the RBF neural networks for chaotic time series prediction. When determining the hidden-layer centers with the unsupervised learning algorithm, a new distance measure is presented based on Gaussian basis, and the strategy of input-output clustering is employed in combination. The punishment factor in Gaussian basis distance is designed based on Fisher separable ratio, which can improve the clustering performance. Moreover, the introduction of input-output clustering strategy establishes the relation between the clustering performance and the prediction performance. Therefore, the RBF neural networks constructed by this method can not only assure the compact structure, but also improve the prediction performance. This method is applied to Mackey-Glass, Lorenz and Logistic chaotic time series prediction, and the results indicate its validity.