支持向量机(SVM)的性能取决于核函数及核参数的选取.基于小波分析理论构造出满足Mercer平移不变核定理的Mexican Hat小波核函数(MW)，将MW和B样条核函数分别与最小二乘支持向量机(LSSVM)结合，形成MW-LSSVM和BS-LSSVM.运用粒子群(PSO)算法对MW-LSSVM和BS-LSSVM的正则化参数及核参数进行智能优化，建立了PSO-MW-LSSVM和PSO-BS-LSSVM的空间风压预测算法.实测风压预测结果表明，MW-LSSVM比BS-LSSVM和传统的径向基核函数RBF-LSSVM具有更好的非高斯风压预测性能及泛化能力，而且稳定性更强，具有较高的工程应用价值.
The performance of support vector machine depends on the selection of kernel functions and kernel parameters. The Mexican Hat wavelet kernel function is constructed based on the wavelet analysis theory which could satisfy the Mercer conditions. Further, the Mexican Hat wavelet kernel function and the B-spline kernel are combined with the LSSVM respectively and accordingly MW-LSSVM and BS-LSSVM are proposed. Subsequently, optimizations for penalty parameters and kernel parameters are conducted using particle swarm optimization (PSO) and thus the PSO-MW-LSSVM and PSO-BS-LSSVM algorithms are proposed for spatial wind pressure prediction. The numerical analysis shows that the proposed method not only significantly outperforms the conventional RBF-LSSVM and BS-LSSVM in forecasting accuracy and generalization ability, but also has great engineering application prospects due to its stability.
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