基于云隶属度支持向量机的舰船购置费时间序列预测

模糊支持向量机降低了传统支持向量机对异常点的敏感度,但其模糊隶属度函数对样本点的分类缺乏模糊性,影响舰船购置费预测的精度。因此,利用云理论能够科学表达模糊性的特点,设计了一种面向异常点模糊分类的云隶属度发生器;在支持向量机中引入这种云隶属度发生器,提出了一种基于云隶属度的支持向量机算法;构建了基于云隶属度支持向量机的舰船购置费时间序列预测模型。实验证明：该算法模糊地降低了模型对异常点的敏感度,并自适应地对支持向量约束水平进行寻优,提高了舰船购置费预测的精度。

Vessel Purchase Cost Time Series Forecasting Based on C-SVM

Vessel purchase cost forecasting faces two problems: small samples and many outliers.Although support vector machines(SVM) is able to forecast small samples,it is too sensitive to fit outliers;fuzzy support vector regression improve that,but the outliers classifactory lack of fuzziness.Using cloud theory which can express fuzziness scientifically,through the improving of backward cloud generator,the cloud membership generator for fuzzy classify of outliers is designed;by transferring the cloud membership into SVM,the algorithm of cloud membership-based SVM(C-SVM) is presented.On this foundation,the model of vessel purchase cost forecasting based on C-SVM is founded.Simulation results prove that C-SVM can not only depress the sensitive of model for outliers fuzzily,but also find the better bound level of support vectors adaptively.The precision of vessel purchase cost forecasting is improved by this model.