|
| Methods for the fuzzy and random probability analysis problem based on Markov Chain and Saddle-point Approximation |
| Received:June 30, 2010 Revised:July 20, 2011 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7511/jslx20122006 |
| KeyWord:random variable fuzzy variable Markov Chain Saddle-point Approximation conditional probability formula |
| Author | Institution |
| 魏鹏飞 |
西北工业大学 航空学院, 西安 |
| 吕震宙 |
西北工业大学 航空学院, 西安 |
| 袁修开 |
西北工业大学 航空学院, 西安 |
|
| Hits: 2860 |
| Download times: 1983 |
| Abstract: |
| Two novel methods are developed to solve the mixed probability analysis problem with both random and fuzzy uncertainties.The first one is Iterative Markov Chain Simulation First Order Saddle-point Approximation (IMCSFOSA) whose key idea is to get the upper limit and lower limit of the reliability for a given membership level with Markov Chain and First Order Saddle-point Approximation,and throughout the whole value domain of membership level by this process,the membership function of reliability can be obtained.Compared with the traditional methods,such as double-loop Monte Carlo simulation and iterative first order and second moment method,the IMCSFOSA is more efficient due to less simulation and more effective due to no transformation from the non-normal distribution to the normal one.The second method is Iterative Conditional Probability Markov Chain Simulation (ICPMCS),in which a nonlinear modification factor is introduced by Conditional Probability formula in solving the upper limit and lower limit of the reliability for a given membership level.The introduction of this factor highly improves the calculation accuracy for the highly nonlinear performance function.Several examples are introduced to illustrate the advantages of the presented methods. |
|
|
|