基于非对称函数的离散混沌序列长期预测算法  

A Long Prediction Algorithm for Discrete Chaotic Series Based on Asymmetric Functions

作　　者：卜云[1] 康万新[1] 汤尚杰 

机构地区：[1]西华大学电气信息学院,四川成都610039 [2]四川省电力公司管理培训中心,四川成都611830

出　　处：《西华大学学报（自然科学版）》2013年第5期55-57,63,共4页Journal of Xihua University:Natural Science Edition

基　　金：四川省教育厅资助项目(12ZB132);西华大学重点基金项目(Z1120944)

摘　　要：离散混沌信号的迭代长期预测是一个困难问题。根据混沌信号概率密度函数的分布特性和分形特征,提出一种使用形状可自适应变化的基函数的预测算法。通过自适应算法改变基函数的形状参数,使函数波形发生任意改变,以模拟混沌信号密度函数的非对称特性,从而使基函数能够反映出混沌信号的更多特征。通过该算法对离散超混沌信号的长期预测的仿真结果表明,本算法能够有效提高预测精度和预测长度。The iteration-based long prediction for discrete chaotic series is a challenging task. According to the density function and the fractal character of a chaotic signal, the authors designed a prediction model by applying shape-variable functions as basis func- tions, which can adjust the shapes automatically, to show the asymmetric property of a chaotic signal statistic density function. The strategy made basis functions provide more information of the chaotic map. In simulation, the algorithm was applied to make a long pre- diction for a hyper discrete chaotic map, and showed an improvement performance in prediction accuracy and length.

关 键 词：概率密度函数 非对称函数 离散混沌序列 长期预测 

分 类 号：TN911.7[电子电信—通信与信息系统]