[1]王定成,倪郁佳,陈北京,等.基于数据依赖核支持向量机回归的风速预测模型[J].南京师大学报(自然科学版),2014,37(03):15.
 Wang Dingcheng,Ni Yujia,Chen Beijing,et al.A Wind Speed Forecasting Model Based on Support VectorRegression with Data Dependent Kernel[J].Journal of Nanjing Normal University(Natural Science Edition),2014,37(03):15.
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基于数据依赖核支持向量机回归的风速预测模型()
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《南京师大学报(自然科学版)》[ISSN:1001-4616/CN:32-1239/N]

卷:
第37卷
期数:
2014年03期
页码:
15
栏目:
计算机科学
出版日期:
2014-09-30

文章信息/Info

Title:
A Wind Speed Forecasting Model Based on Support VectorRegression with Data Dependent Kernel
作者:
王定成倪郁佳陈北京曹智丽
南京信息工程大学计算机与软件学院,江苏 南京 210044
Author(s):
Wang DingchengNi YujiaChen BeijingCao Zhili
College of Computer and Software,Nanjing University of Information Science and Technology,Nanjing 210044,China
关键词:
风速预测数据依赖核支持向量机回归
Keywords:
wind speed forecastingdata dependent kernelsupport vector regression machine
分类号:
TP181
文献标志码:
A
摘要:
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.
Abstract:
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.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2013-12-11.
基金项目:国家自然科学基金(61103141)、江苏省自然科学基金(BK2012858)、江苏省高校自然科学研究资助项目(13KJB520015).
通讯联系人:王定成,博士,研究员,研究方向:智能计算.E-mail:dcwang2005@126.com
更新日期/Last Update: 2014-09-30