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马田系统与SVM相集成的模式识别技术研究
Pattern recognition technology integrating Mahalanobis-Taguchi system and SVM
【摘要】 为了解决现有马田系统阈值确定方法的不足,基于超球面支持向量机算法,构建了单类及二类超椭球面支持向量机算法,并理论上证明了此算法可转换为二次规划模型。推导了基于超椭球面支持向量机的马田系统阈值确定公式。将所提出的方法应用于故障诊断,得到了较高的判别正确率。
【Abstract】 For solving the existing problems of the establishing threshold methods in MTS,applying the theory of hyper sphere support vector machine,the one-class and two-class hyper ellipsoidal support vector machine models are put forward.The two models can be proved theoretically to transform into quadratic programming problems.The function of threshold of MTS based on the hyper ellipsoidal support vector machine is established.At last,a fault diagnosis example is illustrated applying the new method,and high accuracy rating is reached.
【关键词】 马田系统(MTS);
支持向量机(SVM);
马氏距离;
阈值;
故障诊断;
【Key words】 Mahalanobis-Taguchi System(MTS); Support Vector Machine(SVM); Mahalanobis distance; threshold; fault diagnosis;
【Key words】 Mahalanobis-Taguchi System(MTS); Support Vector Machine(SVM); Mahalanobis distance; threshold; fault diagnosis;
- 【文献出处】 计算机工程与应用 ,Computer Engineering and Applications , 编辑部邮箱 ,2010年08期
- 【分类号】TP391.4
- 【被引频次】15
- 【下载频次】211