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Journal of Automated Methods and Management in Chemistry
Volume 2007, Article ID 38405, 6 pages
Research Article

Fuzzy Clustering Neural Networks for Real-Time Odor Recognition System

1Computer Engineering Department, Faculty of Engineering, Fatih University, Istanbul 34500, Turkey
2Computer Engineering Department, Faculty of Engineering, Kültür University, Istanbul 34156, Turkey

Received 23 March 2007; Accepted 7 June 2007

Copyright © 2007 Bekir Karlık and Kemal Yüksek. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The aim of this study is to develop a novel fuzzy clustering neural network (FCNN) algorithm as pattern classifiers for real-time odor recognition system. In this type of FCNN, the input neurons activations are derived through fuzzy c mean clustering of the input data, so that the neural system could deal with the statistics of the measurement error directly. Then the performance of FCNN network is compared with the other network which is well-known algorithm, named multilayer perceptron (MLP), for the same odor recognition system. Experimental results show that both FCNN and MLP provided high recognition probability in determining various learn categories of odors, however, the FCNN neural system has better ability to recognize odors more than the MLP network.