Neural Networks Combined with Importance Sampling Techniques for Reliability Evaluation of Explosive Initiating Device
| Autor: | Jianguo Zhang, Chunlin Tan, Cancan Wang, Qi Gong |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
reliability
Explosive material Artificial neural network Computer science Iterative method business.industry Mechanical Engineering explosive initiating device Sampling (statistics) nonlinearity Aerospace Engineering Probability density function Machine learning computer.software_genre neural networks importance sampling Failure domain Artificial intelligence business computer Algorithm Reliability (statistics) Importance sampling |
| Zdroj: | Chinese Journal of Aeronautics. 25(2):208-215 |
| ISSN: | 1000-9361 |
| DOI: | 10.1016/s1000-9361(11)60380-4 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|