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《Acta Photonica Sinica》 2017-07
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Ungsten Ore Primary Selection Based on Fuzzy Support Vector Machine and D-S Evidence Theory

HU Fa-huan;LIU Guo-ping;HU Rong-hua;DONG Zeng-wen;School of Mechanical & Electrical Engineering,Nanchang University;School of Mechanical & Electrical Engineering,Jiangxi University of Science and Technology;  
According to the low accuracy and low stability of the single feature-based method for tungsten ore primary selection,a multi-feature fusion based on fuzzy support vector machine and D-S evidence theory was proposed.Firstly,the three types of vision feature that is color,gray and texture were extracted from the ore image after a series of image processing.Their probability function were acquired according to each type of feature utilizing fuzzy support vector machine and the results were used to D-S evidence theory as evidence.Finally,using D-S combination rule of evidence to achieve the decision fusion and giving final recognition result by classification rules.The experimental results show that the accuracy of multi-feature fusion methods is over 96% and it has good performance on accuracy and stability compared to the single feature-based method in tungsten ore primary selection.The accuracy and stability can meet the requirement of production process.
【Fund】: 国家自然科学基金(No.71361014)资助~~
【CateGory Index】: TD954;TP391.41
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