摘要
核函数是支持向量机的重要组成部分,直接影响预测模型的结果。根据Mercer定理,推导出了Morlet小波核函数,使其具有局部化、多层次、多分辨的优点。选择具有代表性的径向基(RBF)核函数和多项式(Poly)核函数构建出局部性和全局性相结合的线性组合核函数,使得预测模型保留RBF核函数所赋予的优越学习能力以及Poly核函数所拥有的强泛化能力;进一步,使用粒子群优化(PSO)算法,对惩罚参数、核参数、权重、尺度因子进行寻优,分别建立了基于Morlet小波核和组合核的PSO-LSSVM模型;使用建立的预测模型,对脉动风速进行了预测。通过比较预测性能评价指标,发现基于Morlet小波核和组合核PSO-LSSVM的预测精度优于常用的单核PSO-LSSVM模型。
Kernel functions,which are the important components of support vector machines（ SVM）,directly affect the results of prediction models. In accordance to the Mercer theorem,a Morlet wavelet kernel rendering the advantages of localization,multi-level and mufti-resolution was developed. The representative radial basis function（ RBF）kernel and polynomial（ Poly） kernel functions were taken into consideration to construct a linear combination kernel function with both local and global properties,so as to form prediction models with superior learning ability and perfect generalization capability given by the RBF kernel and Poly kernel functions respectively. Further,the particle swarm optimization（ PSO） algorithm was used to optimize the penalty parameter,kernel parameters and the weight and scale factor. Then,a PSO-LSSVM model using the Morlet wavelet kernel and combination kernel was developed. By resorting to the proposed prediction models,the time histories of fluctuating wind velocity were forecasted. By comparing the predicting performance evaluation indices,it is found that the PSO-LSSVM model with the Morlet wavelet kernel and combination kernel functions renders more accurate results than the common single kernel（ such as Poly and RBF） based PSO-LSSVM models.
出处
《振动与冲击》
EI
CSCD
北大核心
2016年第18期52-57,共6页
Journal of Vibration and Shock
基金
国家自然科学基金(51378304)
关键词
预测
脉动风速
Morlet小波核
组合核
最小二乘支持向量机
粒子群优化
forecasting
fluctuating wind velocity
morlet wavelet kernel
combination kernel
least square support vector machines（LSSVM）
particle swarm optimization
