Interval uncertainty analysis method based on adaptive radial basis function model
Considering the problem of interval uncertainty analysis,an adaptive uncertainty analysis method based on radial basis function model was proposed.Firstly,an acquisition function,also called the potential maximum function,which can be combined with the radial basis function model,was presented,and subdivided into potential maximum/minimum functions according to the characteristics of the maximum/minimum problem.Then,for the interval uncertainty analysis problem,a sequential optimization framework based on the potential maximum/minimum function was established to complete the efficient and high-precision solution of the interval uncertainty analysis problem.Three examples showed that,the proposed method can improve the computational efficiency of particle swarm optimization(PSO)and vertex method with accurate solution;also,the method refined the model sequentially through the proposed acquisition function,so compared with the method in which the Latin hypercube sampling is used to perform the"one-shot"sampling for radial basis function model constructing,and the response bounds is estimated through particle swarm optimization(LHS+PSO),it can guarantee the accuracy of the predicted bounds by improving approximate accuracy of the model in local regions.
radial basis functioninterval modeluncertainty analysisBayesian global optimizationpotential maximum/minimum function
万方数据
维普
