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中文摘要：
为使产品设计时间预测既克服小样本、异方差噪声问题,又提供除预测值以外的其他有用信息,建立概率支持向量回归（PSVR）模型。首先,在异方差回归模型基础上设计概率约束条件,以使预测值以较大概率位于真实值的某邻域,结合具有参数不敏感损失函数的支持向量回归确定优化目标,提出PSVR。然后,将最大完工时间知识嵌入进PSVR的约束条件,用以确定真实值邻域的宽度,将交叉验证与遗传算法相结合以确定PSVR的相关参数。最后,以注塑模具设计的实例进行分析,结果表明基于PSVR的时间预测方法可同时提供有效的预测值和预测区间。
英文摘要：
This paper proposed a forecast model based on probabilistic support vector regression（PSVR） to overcome the problems of small samples and heteroscedastic noise in design time forecast,and provided useful information in addition to the forecast value.Firstly,it designed probability constraints on the basis of heteroscedastic regression model,and made sure that for every sample the forecast value was in a neighborhood of the target value with high probability.It formulated the optimization objective in the form of support vector regression with parameter-insensitive loss function,and proposed PSVR.Then,it embedded prior knowledge of maximum completion time into the constraints of PSVR,and provided the size of the neighborhood of the target value.It applied the combination of genetic algorithm and cross validation to determine the relevant parameters of PSVR.Finally,it analyzed the application in injection mold designs.The results verify that the time forecast method based on PSVR can simultaneously provide effective forecast values and forecast intervals.
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