Neural Networks Combined with Importance Sampling Techniques for Reliability Evaluation of Explosive Initiating Device

查看全文 作　　者：GONG [1]Qi;ZHANG [1]Jianguo;TAN [2]Chunlin;WANG [1]Cancan

机构地区：[1]Reliability System Engineering Institute, Beihang Universitys, Beij ing 100191, China;[2]Beijing lnstitute of Spacecraft System Engineering, Beijing 100081, China

出　　处：《Chinese Journal of Aeronautics》索引2012年第25卷第2期,共8页

基　　金：National Level Project 摘　　要：Concerning the issue of high-dimensions and low-failure probabilities including implicit and highly nonlinear limit state function, reliability analysis based on the directional importance sampling in combination with the radial basis function (RBF) neural network is used, and the RBF neural network based on first-order reliability method (FORM) is to approximate the unknown implicit limit state functions and calculate the most probable point (MPP) with iterative algorithm. For good efficiency, based on the ideas that directional sampling reduces dimensionality and importance sampling focuses on the domain contributing to failure probability, the joint probability density function of importance sampling is constructed, and the sampling center is moved to MPP to ensure that more random sample points draw belong to the failure domain and the simulation efficiency is improved. Then the numerical example of initiating explosive devices for rocket booster explosive bolts demonstrates the applicability, versatility and accuracy of the approach compared with other reliability simulation algorithm. 关键词：径向基函数(RBF)神经网络可靠性评价抽样技术起爆装置联合概率密度函数极限状态函数重要性采样一阶可靠性方法

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Neural Networks Combined with Importance Sampling Techniques for Reliability Evaluation of Explosive Initiating Device

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