计算力学学报2016，Vol.33Issue(4)：495-499,5.DOI:10.7511/jslx201604011
基于更新径向基函数网络模型的广义Pareto分布函数拟合
Generalized pareto distribution based on the radial basis function neural network with tail sample updating
摘要
Abstract
Generalized Pareto Distribution (GPD)is a classical asymptotically motivated model for exce-sses above a high threshold based on the extreme value theory,which is useful for high reliability index estimation.The high computational cost restricts the application of this method.Though the radial basis function neural network (RBFNN)assisted sampling method was proposed to decrease the computa-tional cost,this method may fail when treating highly nonlinear problems.This paper proposes a method for updating the training samples to improve the accuracy of the RBFNN for predicting the tail samples. Compared with the GPD estimation based on the total sample set,the GPD estimation based on the upda-ting RBFNN assisted sampling method can obtain the same results accurately with less computational cost.关键词
广义Pareto分布/径向基函数网络/辅助抽样方法Key words
Generalized Pareto Distribution/radial basis function neural network/assisted sampling method分类
数理科学引用本文复制引用
李刚,赵刚..基于更新径向基函数网络模型的广义Pareto分布函数拟合[J].计算力学学报,2016,33(4):495-499,5.基金项目
973计划课题(2014CB046506) （2014CB046506）
国家自然科学基金(11372016)资助项目 （11372016）
