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| Generalized pareto distribution based on the radial basis function neural network with tail sample updating |
| Received:June 06, 2016 Revised:July 18, 2016 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7511/jslx201604011 |
| KeyWord:Generalized Pareto Distribution radial basis function neural network assisted sampling method |
| Author | Institution |
| 李刚 |
大连理工大学 工程力学系 工业装备结构分析国家重点实验室, 大连 |
| 赵刚 |
大连理工大学 工程力学系 工业装备结构分析国家重点实验室, 大连 |
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| 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 updating RBFNN assisted sampling method can obtain the same results accurately with less computational cost. |