将B(包括B1, B3和B5)样条核函数和径向基(radial basic function, RBF)核函数进行线性组合, 构造B-RBF组合核函数, 进而提出基于粒子群优化B-RBF核的最小二乘支持向量机(least squares support vector machine, LSSVM). 在脉动风速预测中, 运用粒子群优化(particle swarm optimization, PSO)算法对B-RBF-LSSVM模型的惩罚参数和核函数参数进行智能优化. 同时给出PSO-RBF-LSSVM的数值预测结果进行比较. 数值分析表明, PSO-B3-RBF-LSSVM比PSO-B1-RBF-LSSVM, PSO-B5-RBF-LSSVM和PSO-RBF-LSSVM具有更高的预测性能.

With linear combination operation on B-spline kernel functions including B1, B3, and B5 and radial basis function (RBF) kernel functions, combined kernel functions (referred to as the B-RBF function) are proposed. Further, least squares support vector machines (LSSVM) using particle swarm optimization (PSO) based B-RBF functions are developed, termed PSO-B-RBF. To predict fluctuating wind velocity, optimization is implemented on penalty parameters and kernel parameters using the PSO algorithm. For comparison, prediction results of PSO-RBF-LSSVM are also taken into consideration. The numerical analyses demonstrate that PSO-B3-RBF-LSSVM has better performance in predicting fluctuating wind velocity with respect to PSO-B1-RBF-LSSVM, PSO-B5-RBF-LSSVM, and PSO-RBF-LSSVM.