Support Vector Machines Ensemble Based on Fuzzy Integral for Classification

2006 ◽  
pp. 974-980 ◽  
Author(s):  
Genting Yan ◽  
Guangfu Ma ◽  
Liangkuan Zhu
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Fuzzy Integral ◽  
Vector Machines

Study of Fuzzy Integral and Support Vector Machine Algorithm in Machinery Diagnosis

2006 ◽  
Vol 532-533 ◽  
pp. 496-499
Author(s):  
Wangs Shen Hao ◽  
Xun Sheng Zhu ◽  
Jian Cai Zhao ◽  
Biao Jun Tian
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Support Vector ◽  
Fuzzy Integral ◽  
Support Vector Machine Algorithm ◽  
The Neural Network ◽  
Vector Machines ◽  
Diagnosis Accuracy

In the field of fault diagnosis for rotating machines, the conventional methods or the neural network based methods are mainly single symptom domain based methods, and the diagnosis accuracy of which is not always satisfactory. To improve the diagnosis accuracy a method that combines the multi-class support vector machines (MSVMs) outputs with the degree of importance of individual MSVMs based on fuzzy integral is presented. This provides a sound basis for integrating the results from MSVMs to get more accurate classification. The experimental results with the recognition problem of a blower machine show the performance of fault diagnosis can be improved.

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