SVM-RBF网络在混沌时间序列预测中的应用

Application of SVM-RBF to Prediction of Chaotic Time Series

混沌时间序列预测是非线性动力学研究中一个很重要的问题。支持向量机是一种基于统计学习理论的新颖的机器学习方法,为混沌时间序列的预测提供了一种有效的算法思想。本文基于支持向量机与径向基神经网络在结构上的相似性,将支持向量机用于径向基神经网络中心的选取,并对混沌时间序列进行预测,仿真结果表明,其效果优于其他方法。

Prediction of Chaotic Time Series is a vital problem in nonlinear dynamics .Support vector machine （SVM） is a kind of novel machine learning method based on statistical learning theory, which have been provided an efficient algorithm thought in prediction of Chaotic Time Series. This paper based on the similarity of structure between SVM and RBF Networks, using SVM to obtain the centers of RBF Networks, then to predict the Chaotic Time Series. The result performance of simulation is better than other methods.