摘要
为解决基因表达式编程(GEP)在符号回归、RFID分类及经济领域中对时序数据的挖掘速度和精度还不够的问题,提出了统计基因、统计染色体和统计时序-适应度的定义,并针对传统GEP经济时序模型进行了综合改进;提出了新颖的单变量时序和多变量时序挖掘算法,提高了GEP统计时序挖掘的速度和精度;实验表明,与传统GEP、单变量GEP时序算法相比,多变量GEP时序算法挖掘速度快,其预测精度比单变量时序算法高出5%以上。该算法同样适用于RFID以及其他经济系统中的时序数据挖掘。
In order to solve the problem that Gene Expression Programming(GEP) has not still turn up trumps to the mining rapidity and precision of RFID and Economy Statistical Time Sequence Data in symbol regression and class domain,the definition of Statistical-Gene,Statistical-Chromosome,Statistical-fitness and the integration amelioration to traditional GEP time Sequence model were proposed.The novel mining algorithm of single-variable and multi-variable time sequence mining algorithm were given to heighten the mi...
出处
《四川大学学报（工程科学版）》
EI
CAS
CSCD
北大核心
2008年第5期121-124,共4页
Journal of Sichuan University (Engineering Science Edition)
基金
国家自然科学基金资助项目(60473071)
四川省科技攻关资助项目(2006Z01-027)
四川省科技支撑计划资助项目(07GG006-025)
关键词
经济统计时序预测模型
单变量时序
多变量时序
GEP函数挖掘
economy statistical time sequence forecast model
single-variable GEP time sequence
multi-variable time sequence
Gene Expression Programming function mining
