基于灰色GM(1,1)的优化模型预测大坝形变  

Based on Gray Model Optimization GM (1,1) to Predict the Dam Deformation

作　　者：李伟[1] 杨兆臣[1] 张遇[1] 

机构地区：[1]山东科技大学测绘科学与工程学院,山东青岛266590

出　　处：《全球定位系统》2014年第3期67-70,73,共5页Gnss World of China

摘　　要：以往的灰色GM(1,1)优化模型往往忽略了灰度模型随时间变化的灰色量和灰色过程这一重点,其中的灰色作用量是时间的线性函数,传统的模型把其看为不变常数,而对时间的精度影响没做深层次的考虑,建模时较少的考虑时间相关度和灰作用量的影响,势必会影响预测精度,造成模型效果欠佳,故本文尝试对这两个因素分别建立优化GM(1,1)模型,即引入基于时间的加权R-GM(1,1)模型和用灰作用量b1+b2k替代b的K-GM(1,1)模型,并对实例数据进行分析,取得了较好的拟合与预测能力。In the past ,T he Grey GM （1 ,1） optimization model tends to ignore the key a-bout the gray quantity of gray model with time-varying and grey process .The Grey action is a linear function of time ,traditional models to look at it as a fixed constant ,and the effect on the accuracy of the time did not do in-depth consideration ,w hen structure model less consid-er about time correlation or the influence about Grey action ,and certainly affect the predic-tion accuracy ,these effects will cause a poor model ,therefore ,this paper attempts to opti-mize these two factors are building an optimized GM （1 ,1） model .Introducing R-GM （1 ,1） model which based on the time-weighted and K-GM （1 ,1） model which the gray action b re-placed by ,and the analysis of the instance data is presented .A better fitting and predictive a-bility are obtained .

关 键 词：灰度模型 形变预测 R-GM(1 1)模型 K-GM(1 1)模型 

分 类 号：TV698.1[水利工程—水利水电工程]