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• W2040272362 abstract "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." @default.
• W2040272362 created "2016-06-24" @default.
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• W2040272362 date "2012-04-01" @default.
• W2040272362 modified "2023-09-28" @default.
• W2040272362 title "Neural Networks Combined with Importance Sampling Techniques for Reliability Evaluation of Explosive Initiating Device" @default.
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• W2040272362 doi "https://doi.org/10.1016/s1000-9361(11)60380-4" @default.
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