长江科学院院报2016，Vol.33Issue(2)：48-51,4.DOI:10.11988/ckyyb.20140801
基于IP SO-RVM的大坝安全预警模型
Prewarning Model for Dam Safety Based on IPSO-RVM
摘要
Abstract
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 ba⁃sis 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Key words
dam safety modeling/relevance vector machine/hybrid kernel function/adaptive particle swarm opti-mization/fitting accuracy/generalization ability分类
建筑与水利引用本文复制引用
范振东,崔伟杰,陈敏,杜传阳..基于IP SO-RVM的大坝安全预警模型[J].长江科学院院报,2016,33(2):48-51,4.基金项目
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 （）
