集成灰色支持向量机预测模型研究与应用  

Research and application of integrated grey support vector machine model

作　　者：林耀进[1] 周忠眉[1] 吴顺祥[2] 

机构地区：[1]漳州师范学院计算机科学与工程系,福建漳州363000 [2]厦门大学自动化系,福建厦门361005

出　　处：《计算机应用》2009年第12期3287-3289,共3页journal of Computer Applications

基　　金：国家自然科学基金资助项目(60704042);国家"十一五"科技支撑计划项目(2007BAK34B04);福建省自然科学基金资助项目(2007J0039)

摘　　要：对灰色预测GM(1,1)模型进行了分析,提出了集成灰色支持向量机的预测模型。分别对影响灰色预测GM(1,1)模型精度的背景值的计算、初值的选取以及数据序列的光滑度进行改进,提出了背景GM模型、初值GM模型、光滑度GM模型,并结合支持向量机的特点,将一维原始数据序列通过三个灰色模型得到的三组值作为支持向量机的输入,原始序列作为支持向量机的输出,训练得到最佳支持向量回归机模型。仿真结果表明了该模型的有效性。Based on grey prediction GM （ 1, 1） model, an integrated grey Support Vector Machine （SVM） model was presented. Through improving the GM （1, 1 ） prediction accuracy based on background value calculation, initial value selection and smooth degree of data sequence, three grey prediction models that are background GM model, initial value GM model, smooth degree GM model, were put forward. Then, combining the advantages of SVM, the prediction results of three grey prediction models were used as the SVM input factor, and the original data sequence was used as the output factor of the SVM. The support vector regression machine was trained to get the optimal structure. The results of experiment show that the model is valid.

关 键 词：灰色系统 支持向量机 预测 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]