基于RFID与基因表达式编程的经济统计时序挖掘

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.

RFID and Economy Statistical Time Sequence Data Mining Based on Gene Expression

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 mining rapidity and precision of GEP economy time sequence model. The effectiveness of new algorithm was demonstrated by extensive experiments and the result showed that the mining rapidity of multi-variable time sequence mining algorithm was rapidness and the forecast precision was heighten up 5% compared with traditional GEP and single variable GEP time sequence mining algorithm. New algorithm was appropriate for RFID and other economy system as well.