【摘要】 在分析支持向量机(Support Vector Machines,SVM)回归算法的基础上,提出了一种免疫遗传优化向量机回归算法来预测混沌时间序列。算法根据混沌时间序列样本的特点利用免疫遗传算法对支持向量机回归方法参数进行优化。免疫遗传算法有效地克服了未成熟收敛现象,获得相关参数最优值。对混沌时间序列预测的实际算例表明,与支持向量机方法相比,本文所提的免疫遗传优化支持向量机回归方法具有更高的预测精度。更多还原

【Abstract】 On the basis of analyzing the parameter performance of support vector machines (SVM), an genetic immune support vector method for Chaotic Time Predicting. The Algorithm use is the gentic immune algrithm to optimize the parameters in SVM.The algorithm can effectively surmount the premature convergence and promote the diversity of colony and Obtain optimal parameters values.The calculation results from Chaotic Time Predicting example show that the presented the gentic immune SVM method can offer more accurate forecasting result than SVM method.更多还原