关键词: 径向基代理模型技术, 拉丁超立方设计, 多策略建模技术, 融合核函数, 仿真分析

Abstract: Aiming at the poor prediction performance of Radial Basis Function（RBF） metamodel technology when approximating high-dimensional problems, an improved technology based on Hybrid kernel function Radial Basis Function（HRBF） is proposed. In the case of the uneven distribution of samples of the Latin Hypercube Sampling（LHS）, an Uniform Latin Hypercube Sampling（ULHS）design is proposed by defining an auxiliary function and distance evaluation criteria and used in the construction of RBF. To improve model prediction accuracy and computational efficiency, modeling technology based on Multi Strategy Radial Basis Function（MSRBF） composed of local intensive adding-points, global uniform sampling and minimum distance filtering strategy is used to construct model considering sample point factor; meanwhile, to avoid structural risk when MSRBF is used to approximating high-dimensional problems, considering the influence of structural factors, the weighted hybrid of inverse multiquadratic and cubic kernel functions is performed, and an improved RBF metamodel based on fusion kernel function is constructed. Simulation experiments are carried out using numerical and engineering examples, results show that HRBF not only meets the accuracy requirements, but also significantly improves the computational efficiency and has higher prediction stability.

Key words: radial basis function metamodel technology, Latin hypercube sampling design, modeling technology based on multi strategy, hybrid kernel function, simulation analysis