【摘要】 针对支持向量机核函数和控制参数选取难度较大的问题,提出了一种主动划分参数区间的双尺度径向基核支持向量机,并用并行定向变异混合粒子群优化算法选取其控制参数。试验分析了利用标准数据集经多次独立重复试验得到的均值等统计量,验证、测试了上述支持向量机模型,同时考虑了类间数据不平衡的影响。结果表明,双尺度径向基核函数的性能在多数情况下优于单径向基核函数,并行定向变异的混合粒子群优化算法优于标准粒子群优化算法,能够有效抑制早熟收敛,有利于搜索到更优的支持向量机控制参数。更多还原

【Abstract】 In light of existing problems of the support vector machine,such as the difficulties of selecting its kernel function and control parameters,a dual-scale radial basis kernel support vector machine model is proposed that is optimized by hybrid particle swarm optimization based on parallel directional turbulence.The parametric intervals of the kernel have been actively divided.By utilizing the standard data set,statistical quantities such as mean value were acquired by multiple dependable and repeatable trials,and have tested and validated the support vector machine model.The data imbalance between difference classes had been considered during experiments.The test results show that in most cases,the performance of dualscale radial basis kernel functions is better than single ones,and hybrid particle swarm optimization based on parallel directional turbulence is better than the common particle swarm optimization,because it can effectively restrain premature convergence and be beneficial in searching for better control parameters of the support vector machine.This method has been applied well to the fault diagnosis of engines.更多还原