基于选择性支持向量机集成的混沌时间序列预测

提出了一种基于聚类的选择性支持向量机集成预测模型.为提高支持向量机集成的泛化能力，采用自组织映射和K均值聚类算法结合的聚类组合算法，从每簇中选择出精度最高的子支持向量机进行集成，可以保证子支持向量机有较高精度并提高了子支持向量机之间的差异度.该方法能以较小的代价显著提高支持向量机集成的泛化能力.采用该方法对Mackey-Glass混沌时间序列和Lorenz系统生成的混沌时间序列进行预测实验，结果表明可以对混沌时间序列进行准确预测，验证了该方法的有效性. 关键词： 支持向量机 集成 混沌时间序列 聚类

Prediction of chaotic time series based on selective support vector machine ensemble

A clustering-based selective support vector machine ensemble forecasting model is presented. For improving the generalization ability of support vector machine ensemble, a hybrid clustering algorithm which combines the SOM and K-means algorithm is used to select the most accurate individual support vector machine from every cluster for ensembling, which ensures accuracy of individual support vector machines and improves the diversity of the individual support vector machines. This method can improve support vector machine ensemble generalization ability effectively with low cost. To illustrate the performance of the proposed forecasting model, simulations on chaotic time series prediction of the Mackey-Glass time series and the time series generated by the Lorenz systems are performed. The results show that the chaotic time series are accurately predicted, which demonstrates the effectiveness of this method.