基于遗传算法的偏最小二乘回归模型在大坝安全监测中的应用  

Modeling of Partial Least-Squared Regression and genetic algorithm in dam safety monitoring analysis

作　　者：邓念武[1] 陈正[1] 叶泽荣[1] 

机构地区：[1]武汉大学水资源与水电工程科学国家重点实验室,湖北武汉430072

出　　处：《大坝与安全》2007年第4期33-35,共3页Dam & Safety

摘　　要：偏最小二乘回归能较好地解决自变量之间严重的相关性问题,遗传算法作为一种新的全局优化搜索方法,具有智能性搜索、并行式计算、鲁棒性强等优点。本文在偏最小二乘回归分析的基础上引入遗传算法,依靠其有效的自适应全局搜索优化功能,对偏回归模型中的回归系数进行重新评估,建立基于遗传算法的偏回归模型。实例分析表明:基于遗传算法的偏回归模型有良好的拟合效果和预测精度。Partial Least-Squared（PLS） Regression can resolve multicollinearity of importance. As a new type of searching method for global optimization, genetic algorithm has some merits such as intelligence searching, parallel mode and robust. After modeling by PLS regression, the coefficient is re-evaluated by the genetic algorithms depended on its effective self-adapting global search- ing optimization, at last PLS regression model based on genetic algorithms is obtained. The modeling is used and the results show that the modeling attains good simulating effect and forecasting precision.

关 键 词：大坝监测模型 遗传算法 偏最小二乘回归 

分 类 号：TV698.1[水利工程—水利水电工程]