Abstract:

The traditional adaptive importance sampling method based on Markov chain Monte Carlo (MCMC) can only be applied to the system of determined failure domain but not to the gradual structure system of fuzzy failure domain. A new fuzzy adaptive importance sampling method based on MCMC is proposed. Firstly, the Markov chain samples are constructed according to Metropolis algorithm from the initial sample in the failure domain. Then a kernel sampling probability density function is obtained by adaptive kernel density estimation and the importance sampling is carried out. Finally, the fuzzy failure domain is discretized to compute the fuzzy failure probability. This approach solves the problems that the performance reliability of gradual structure systems is hard to be analyzed through simulation, and the efficiency of simulation is very low. The feasibility and effectiveness of this method are demonstrated by the case of the actuator system.