Method of reliability analysis on complex systems based on support vector machine and evidence theory
This paper presents an efficient reliability analysis method based on support vector machines(SVM)and evi-dence theory to address the challenges posed by implicit limit state equations and cognitive uncertainty in complex systems.Firstly,using Bayesian methods and the maximum entropy principle,the basic probability assignment on focal elements is even-ly distributed to each element to achieve the refinement of evidence bodies.Secondly,when facing implicit limit state equations in multidisciplinary systems,support vector machines(SVM)are employed to explicitly handle them.In this method,an SVM training sample extraction strategy is proposed,and an improvement is made to the SVM by introducing the Markov Chain Monte Carlo(MCMC)simulation method,enabling it to solve the small failure probability of implicit limit state equations in multidisciplinary systems.Finally,through case studies,the accuracy and computational efficiency of the proposed method are demonstrated,showing significant advantages compared to Monte Carlo simulation(MCS).With a sample size of 2 000 points,the proposed method achieves an accuracy error of only 3.05％,providing valuable insights for reliability analysis of complex systems.
support vector machineevidence theoryMarkov Monte Carlo simulationcomplex system
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