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中文摘要：
针对产品销售时序具有多维度、非线性的特征,通过设计一种扩展的径向基函数核函数,将其应用于支持向量机中,得到一种扩展的径向基函数核支持向量机;设计了一种改进的免疫优化算法对其参数进行寻优。将该方法应用于汽车销售预测实例中,并与反向传播神经网络、采用一般径向基函数核的支持向量机及多尺度支持向量机进行了比较。实验结果表明该方法可行有效,其预测精度优于其他三种方法。
英文摘要：
Aiming at the characteristics of multi-dimension and nonlinearity existing in the product sale series, an expanded Radial Basis Function （RBF） kernel was designed and applied to Support Vector Machine （SVM） to get an Expanded RBF kernel Support Vector Machine （ERBF-SVM）, and an improved immune optimization algorithm was designed to optimize the parameters of ERBF-SVM. The proposed method was applied to the automobile sales forecasting in contrast with Back Propagation Neural Network （BPNN）, SVM of RBF and Multi-Scale Support Vector Machine （MS-SVM）, The experiment results indicated that ERBF-SVM was effective and feasible, and the predic- tion accuracy was better than other three methods.
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