A multiwavelet support vector regression method for efficient reliability assessment

As a new sparse kernel modeling technique, support vector regression has become a promising method in structural reliability analysis. However, in the standard quadratic programming support vector regression, its implementation is computationally expensive and sufficient model sparsity cannot be guaranteed. In order to mitigate these difficulties, this paper presents a new multiwavelet linear programming support vector regression method for reliability analysis. The method develops a novel multiwavelet kernel by constructing the autocorrelation function of multiwavelets and employs this kernel in context of linear programming support vector regression for approximating the limit states of structures. Three examples involving one finite element-based problem illustrate the effectiveness of the proposed method, which indicate that the new method is efficient than the classical support vector regression method for response surface function approximation.

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