CAJ | 학술논문

针对风速随机性大、影响因素多、预测准确度不高的情况,基于支持向量机与信息几何的统计学关联性,从信息几何学角度分析核函数的几何结构,构造数据依赖核函数,并与支持向量机回归相结合,形成数据依赖核支持向量机回归( Data Dependent Kernel-SVR,DDK-SVR)方法。将该方法用于风速预测中,建立DDK-SVR风速预测模型,并将预测结果与传统支持向量机、神经网络方法进行对比。结果表明,DDK-SVR方法具有更高的预测精度。
침대풍속수궤성대、영향인소다、예측준학도불고적정황,기우지지향량궤여신식궤하적통계학관련성,종신식궤하학각도분석핵함수적궤하결구,구조수거의뢰핵함수,병여지지향량궤회귀상결합,형성수거의뢰핵지지향량궤회귀( Data Dependent Kernel-SVR,DDK-SVR)방법。장해방법용우풍속예측중,건립DDK-SVR풍속예측모형,병장예측결과여전통지지향량궤、신경망락방법진행대비。결과표명,DDK-SVR방법구유경고적예측정도。
Wind is random and has many factors. Besides,the prediction accuracy of wind is not high. Therefore,based on the statistics relationship between Support Vector Machine( SVM) and information geometry,the geometry of kernel function is analyzed. A data dependent kernel is constructed and combined with Support Vector Regression( SVR) . Then,the support vector regression machine with data dependent kernel is proposed. We build a wind speed forecasting model and forecast the wind speed. Compared with SVM and neural networks,DDK-SVR method has higher prediction accuracy.