长江科学院院报2018，Vol.35Issue(5)：57-62,6.DOI:10.11988/ckyyb.20161313
基于AdaBoost集成的WPSO-RBF大坝变形监控模型
Dam Deformation Monitoring by Radial Basis Function Model Optimized by Particle Swarm Optimization with Inertia Weight and AdaBoost
摘要
Abstract
Deformation monitoring is a requisite for dam safety monitoring. Due to a large number of factors,neural networks such as back propagation (BP) and radial basis function (RBF) are often used for parameters selection and model establishment,of which RBF has been widely employed on account of its simple network structure and rapid convergence. Nonetheless,local optimality and inappropriate selection of parameters will exert great impact on the convergence rate. In view of this,the Particle Swarm Optimization with Inertia Weight (referred to as WPSO) is adopted to optimize three parameters of RBF (central value c of hidden layer base function parameter, width d and connection weight w between hidden layer and output layer parameter). In subsequence,the WPSO-RBF mod-el is integrated as a weaker classifier by AdaBoost algorithm,hence establishing a WPSO-RBF-AdaBoost model for dam deformation monitoring. The model is applied to practical engineering, and results suggest that the present model is of fast convergence,high classification precision and good generalization ability.关键词
大坝变形/监控模型/改进粒子群算法/RBF神经网络/AdaBoost算法Key words
dam deformation/monitoring model/Particle Swarm Optimization with Inertia Weight/RBF neural network/AdaBoost algorithm分类
建筑与水利引用本文复制引用
沈晶鑫,房彬,郑东健,郭芝韵,李丹..基于AdaBoost集成的WPSO-RBF大坝变形监控模型[J].长江科学院院报,2018,35(5):57-62,6.基金项目
国家自然科学基金项目(51279052,51579085) （51279052,51579085）
水文水资源与水利工程科学国家重点实验室研究项目(20145028312) （20145028312）
中央高校基本科研业务费专项(2015B32514,2015B33314) （2015B32514,2015B33314）
