Fuzzy c-means based support vector machines classifier for perfume recognition期刊界 All Journals 搜尽天下杂志传播学术成果专业期刊搜索期刊信息化学术搜索

Fuzzy c-means based support vector machines classifier for perfume recognition

Affiliation:	1. Selcuk University, Vocational School, Kulu-Konya, Turkey;2. Beykent University, Faculty of Engineering and Architecture, Istanbul, Turkey;1. Department of Computer Science and Artificial Intelligent, University of Granada, C/ Daniel Saucedo Aranda, s/n, 18071 Granada, Spain;2. European Centre for Soft Computing, C/ Gonzalo Gutierrez Quirós, s/n, 33600 Mieres, Asturias, Spain;1. The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan, Hubei 430079, China;2. Collaborative Innovation Center for Geospatial Technology, 129 Luoyu Road, Wuhan, Hubei 430079, China;3. The Xinjiang Institute of Ecology and Geography, Chinese Academy of Science, 818 South Beijing Road, Urumqi, XinJiang 830011, China;4. Satellite Surveying and Mapping Application Center, NASG, Beijing 101300, China;1. Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, PR China;2. School of Automation, Huazhong University of Science and Technology, Wuhan 430074, PR China;3. School of Mechatronic Engineering and Automation, Shanghai University, 200072, PR China

Abstract:	Identification of more than three perfumes is very difficult for the human nose. It is also a problem to recognize patterns of perfume odor with an electronic nose that has multiple sensors. For this reason, a new hybrid classifier has been presented to identify type of perfume from a closely similar data set of 20 different odors of perfumes. The structure of this hybrid technique is the combination of unsupervised fuzzy clustering c-mean (FCM) and supervised support vector machine (SVM). On the other hand this proposed soft computing technique was compared with the other well-known learning algorithms. The results show that the proposed hybrid algorithm’s accuracy is 97.5% better than the others.

Keywords:	Classifier Soft computing Perfume recognition Fuzzy c-means Support vector machines Artificial neural network
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