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将主成分分析与支持向量机结合应用到多品种小批量产品的质量预测。首先确定多品种小批量产品生产过程中的定量影响因素,并将其作为初始影响因素集;然后利用主成分分析方法降低因素集的维度,同时提取关键主成分;最后将关键主成分作为影响因素集并建立针对于多品种小批量生产的支持向量机质量等级预测模型。算例分析表明,与传统的支持向量机分类模型相比,主成分分析与支持向量机结合的模型预测准确率及稳定性均有显著提高,说明模型具有更好的预测性能。
Applying principal component analysis and support vector machine to the quality prediction of small variety of many varieties. Firstly, the quantitative factors affecting the production of small variety and small batch products are determined and used as the initial set of influencing factors. Then, the principal component analysis is used to reduce the dimension of the factor set and extract the key principal components. Finally, the key principal components are taken as the influencing factors Set up and set up the support vector machine quality level forecasting model for many kinds of small batch production. The case study shows that compared with the traditional SVM classification model, the prediction accuracy and stability of the model based on principal component analysis and support vector machine are significantly improved, which shows that the model has better prediction performance.