【摘要】 从中药提取工艺的历史数据中,挖掘确定提取参数的相关知识,用于指导工艺人员选择正交试验的影响因素及各因素水平。采用决策树ID3算法和支持向量分类算法,构建了提取次数的分类器;采用支持向量回归算法分别为提取时间和溶媒量建立了回归预测模型。实验结果表明,尽管ID3算法的结果可理解性较好,但支持向量分类算法有更高的精度;支持向量回归算法建立的预测模型是可靠的。更多还原

【Abstract】 Knowledge for selecting extracting parameters was mined from past data of extracting technology of traditional Chinese pharmacy , and it could be used to direct technologist to select appropriate factors and factor levels of orthogonal test. Classifier taking extracting times as target attribute was constructed by decision tree ID3 and support vector classification algorithm. Support vector regression algorithm was applied to construct predict models for extracting time and the volume of the solvent respectively. Experiment results show that support vector classification algorithm has higher accuracy as though the output of ID3 is more comprehensive, and the predict model constructed by support vector regression is reliable.更多还原