Modification of SVM classification fuzzy area based on DS evidence theory
LI Jiangyou · Dianli zidonghua shebei · 2012
SVM(Support Vector Machine) and DS evidence theory are introduced and the method to modify the data of SVM fuzzy classification areas is proposed based on DS evidence theory.The test samples are classified by SVM and the distance of its output fuzzy area samples is transformed into their membership grades to each state by the membership function.The diagnosis information of other sensors is fused together based on the DS evidence theory to modify the sample membership grades to each state and the data of this area is redistributed.The proposed method is applied in the fault diagnosis of high voltage circuit breakers for verifying its diagnostic performance.Experimental results show that,it uses the data of coil current to modify the results of vibration data classification to realize the accurate detection of mechanical fault.