新型支持向量机在风速预测模型中的应用研究

在短期风速预测方面支持向量机已被广泛应用并取得较好的效果. 然而, 随着应用的深入, 其逐渐暴露出两大问题: 一, 对噪声较为敏感; 二, 未能充分利用样本已有信息. 为进一步提高支持向量机的泛化能力, 该文提出模糊流形支持向量机FMSVM. 该方法引入模糊技术, 保证不同样本区别对待, 减少或消除噪声的影响; 充分利用流形判别分析的性质, 进一步改进支持向量机, 在分类决策时同时考虑样本的边界信息、分布特征以及局部流形结构. 通过某风场风速数据集上的比较实验验证该方法的有效性.

Research on Wind Speed Forecasting Model Based on Novel Support Vector Machine

Support vector machine (SVM) is widely used in wind speed forecasting and the forecasted results are verified well. With the applications get more intensive, there exist two problems in SVM. One is it is too sensitive to noises and the other is it can not fully use the information included in the samples. In view of this, a fuzzy manifold-based support vector machine (FMSVM) is proposed in this paper to solve the above problems and further improve the generalization capability of SVM. FMSVM introduces the fuzzy techniques to decrease the influence of the noises. Meanwhile, FMSVM takes boundary data between classes, data distributions and manifold seriously. The comparative experiments show that FMSVM performs better than SVM on the wind datasets of a certain wind farm.