摘要
为了提高混沌时间序列预测模型的预测精度,提出一种基于差分进化(DE)算法的相空间重构和最小二乘支持向量机(LSSVM)模型参数组合优化方法.该方法将相空间重构参数和LSSVM预测模型参数进行组合作为差分进化算法的个体,以混沌时间序列预测精度作为个体的适应度函数,通过循环迭代获得最优参数组合.几个混沌时间序列的仿真实验结果表明,与传统的优化方法相比,参数组合优化方法具有更高的预测精度.
To improve the prediction accuracy of the chaotic time series prediction model, a composite optimization method of the differential evolution （DE） algorithm that is based on the phase space reconstruction and least square supported vector machine （LSSVM）, is proposed. The phase space parameters and LSSVM model parameters are taken as differential evolution algorithm individuals while the prediction accuracy of the chaotic time series is used as the evaluation function of DE algorithm. The optimal parameters are obtained by mutation, crossover, and selection operators of DE algorithm. Several numerical simulation results show that not only four parameters are determined at the same time, but also the performance of chaotic time series prediction is improved.
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2012年第22期120-126,共7页
Acta Physica Sinica
基金
国家自然科学基金(批准号:10961008)资助的课题~~
关键词
混沌时间序列
差分进化算法
参数组合优化
预测
chaotic time series, differential evolution algorithm, parameter composite optimization, prediction
