基于IPSO-RVM的大坝安全预警模型  

Prewarning Model for Dam Safety Based on IPSO-RVM

作　　者：范振东[1,2] 崔伟杰 陈敏[1,2] 杜传阳 

机构地区：[1]河海大学水文水资源与水利工程科学国家重点实验室,南京210098 [2]河海大学水利水电学院,南京210098 [3]雅砻江流域水电开发有限公司,成都610051

出　　处：《长江科学院院报》2016年第2期48-51,共4页Journal of Changjiang River Scientific Research Institute

基　　金：国家自然科学基金项目(51179066);高等学校博士学科点专项科研基金资助课题项目(20130094110010);江苏省杰出青年基金项目(BK2012036);水利部公益性行业科研专项经费项目(201301061)

摘　　要：针对SVM(Support Vector Machine,支持向量机)存在支持向量个数较多、核函数要求严格等不足,将性能更出色的RVM((Relevance Vector Machine,相关向量机)用于大坝安全预警模型的构建。核函数及其参数对RVM模型的性能有着重要的影响,组合局部核函数和全局核函数的混和核函数能提高模型的拟合精度和泛化能力,利用PSO(Particle Swarm Optimization,粒子群算法)能有效地对核参数进行寻优,针对标准PSO算法容易陷入局部最优点的缺陷,提出IPSO(Improved Particle Swarm Optimization,改进的粒子群算法)。将上述组合算法用于大坝安全模型的建立,实例分析表明,基于上述算法模型的性能得到了一定程度的提高。In view of the disadvantages of SVM（ support vector machine） such as a large number of support vectors and strict demand for kernel function,we introduce RVM（ relevance vector machine） to establish dam safety model which has better performance. Kernel function and its parameters have important effects on the performance of the RVM model. Mixed kernel function in association with local and global kernels can improve the fitting accuracy and generalization ability of the model. The optimized parameters of the kernel function can be effectively found by using PSO（ particle swarm optimization） algorithm. However,the defect of local optimal point easily occurs in normal PSO algorithm. In light of this,we apply an algorithm of improved particle swarm optimization（ IPSO）. On the basis of combined algorithms above,we establish a model for dam safety,and the results indicate that the performance of RVM model with hybrid kernel is superior to that of conventional model.

关 键 词：大坝安全建模 相关向量机 混合核函数 自适应粒子群算法 拟合精度 泛化能力 

分 类 号：TV698.1[水利工程—水利水电工程]