摘要
首先简单介绍了支持向量机的概念,接着以某流程型企业的具体决策支持系统为应用背景,论述了支持向量机在伙伴企业选择、生产预警故障诊断中的应用。在伙伴企业选择方面使用了支持向量机的回归算法,在生产预警和故障诊断方面使用了支持向量机的分类算法,其中,还配合使用主成分分析方法,对学习样本起降维降噪作用。实验证明,采用支持向量机方法,不仅具有较高的训练效率,而且有更高的精确度。
This paper presentes the principles of the Support Vector Machine.Based on a concrete Decision Support System of certain process manufactory enterprise,it also discusses the application of SVM in the Partner Enterprise Selection and Fault Diagnosis.Two algorithm are concerned,the regression algorithm in the Partner Enterprise Selection and the classification algorithm in Fault Diagnosis.Besides,Principal Component Analysis algorithm,which is applied to lessen dimensions and decrease noise of input space of the training set,is also discussed.It is proved that SVM can respectively improve not only the training efficiency but also the training accuracy.
出处
《计算机工程与应用》
CSCD
北大核心
2004年第23期209-211,共3页
Computer Engineering and Applications
基金
上海市科委基金项目资助
