岩土力学2012，Vol.33Issue(5)：1421-1426,6.
基于混合核函数PSO-LSSVM的边坡变形预测
Forecasting of slope displacement based on PSO-LSSVM with mixed kernel
摘要
Abstract
The kernel and the parameters of support vector machine (SVM) have a significant impact on precision of the time series prediction to slope displacement. In view of better learning capability of local kernels and better generalization capability of global kernels, the mixed kernel is constructed by a typical local kernel-radial basis function (RBF) and a typical global kernel-polynomial kernel. By use of particle swarm optimization (PSO), a new PSO-LSSVM model regression with mixed kernels is set up in this paper and applied to the left bank slope in Jinping I Hydropower Station. Through comparing with the forecasting results of the existing SVM based on RBF, results demonstrate that the new model has great accuracy than the existing SVM with RBF only and has real application value in predicting deformations of slope.关键词
边坡/边坡变形预测/最小二乘支持向量机/粒子群优化/混合核Key words
slope/ prediction of slope deformation/ least squares support vector machines/ particle swarm optimization/ mixed kernel分类
建筑与水利引用本文复制引用
郑志成,徐卫亚,徐飞,刘造保..基于混合核函数PSO-LSSVM的边坡变形预测[J].岩土力学,2012,33(5):1421-1426,6.基金项目
国家科技支撑计划(No.2008BAB29B01) （No.2008BAB29B01）
江苏省普通高校研究生科研创新计划(No.CX09B-158Z) （No.CX09B-158Z）
国家自然科学基金项目(No.50909038). （No.50909038）
