摘要
传统的预测建模方法有曲线拟合、线性回归分析等,这些方法通常只适用于求解结构简单的多项式函数。该文采用基因表达式程序设计方法,该算法简便、易于遗传操作,并且其搜索空间广阔,函数复杂度高,能广泛适用于各种类型的数据流预测。在此基础上,提出当预测模型失效时的大变异策略,收到了很好的效果。
Many traditional methods in the field of forecasting, including curve simulation, linear regression, etc, which are applied only to solve simple polynomial functions. Adopting gene expression programming （GEP）, this paper proposes a predictive mathematical model for forecasting the aggregatde value over data streams. The algorithm is simple and easy to operate which search functions in the great space. As a result, this forecasting model can be used in many kinds of the data stream. When the frequency of forecast failing is greater than a predefined threshold, an adaptive strategy for the predictive mathematical model is proposed.
出处
《计算机工程》
CAS
CSCD
北大核心
2007年第18期75-77,92,共4页
Computer Engineering
关键词
数据流
预测查询
基因表达式程序设计
函数模型流
大变异策略
data stream
predictive query
gene expression programming （GEP）： function model stream： great mutation strategy
