一种联合优化算法及其在混沌时间序列预测中的应用

为了提高混沌时间序列预测精度,利用相空间重构和预测模型参数间的相互联系,提出一种基于遗传算法的混沌时间序列参数联合优化方法.该方法首先将相空间重构和预测模型参数作为遗传算法的个体,混沌时间序列预测精度作为适应度函数,通过选择、交叉和变异等遗传操作获得最优参数,最后利用混沌时间序列实例对联合优化方法进行验证性测试.实验结果表明:相对于传统参数优化方法,联合优化方法大幅度提高混沌时间序列的预测精度,为混沌时间序列预测提供一种新的思路.

A joint optimization algorithm and its application in chaotic time series prediction

To improve the prediction precision of the chaotic time series,a joint optimization algorithm for chaotic time series parameters was proposed based on the genetic algorithm,using the relationship between the phase space reconstruction and prediction model parameters.Firstly,the phase space reconstruction and prediction model parameters were taken as genetic algorithm individuals while the prediction accuracy of the chaotic time series was taken as the evaluation function of genetic algorithm.Secondly,the optimization parameters were obtained by selection,crossover and mutation of genetic algorithm.Finally,the joint optimization algorithm was tested by chaotic time series.The results show that the joint optimization algorithm improves the prediction precision compared with the traditional parameters optimization algorithm,and provides a new way for the chaotic time series prediction.