Fuzzy Clustering Neural Networks for Real-Time Odor Recognition System

Karlık, Bekir; Yüksek, Kemal

doi:https://doi.org/10.1155/2007/38405

Journal of Analytical Methods in Chemistry

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Research Article | Open Access

Volume 2007 | Article ID 038405 | https://doi.org/10.1155/2007/38405

Fuzzy Clustering Neural Networks for Real-Time Odor Recognition System

Bekir Karlık¹and Kemal Yüksek²

Received23 Mar 2007

Accepted07 Jun 2007

Published15 Nov 2007

Abstract

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.

References

P. Keller, L. Kangas, L. Liden, S. Hashem, and R. Kouzes, “Electronic noses and their applications,” in Proceedings of the World Congress on Neural Networks (WCNN '96), pp. 928–931, San Diego, Calif, USA, September 1996.
View at: Google Scholar
T. Nakamoto, Y. Nakahira, H. Hiramatsu, and T. Moriizumi, “Odor recorder using active odor sensing system,” Sensors and Actuators B, vol. 76, no. 1–3, pp. 465–469, 2001.
View at: Publisher Site | Google Scholar
A. K. Pavlou, N. Magan, and C. McNulty et al., “Use of an electronic nose system for diagnoses of urinary tract infections,” Biosensors and Bioelectronics, vol. 17, no. 10, pp. 893–899, 2002.
View at: Publisher Site | Google Scholar
T. Nakamoto and H. Hiramatsu, “Study of odor recorder for dynamical change of odor using QCM sensors and neural network,” Sensors and Actuators B, vol. 85, no. 3, pp. 263–269, 2002.
View at: Publisher Site | Google Scholar
P. E. Keller, R. T. Kouzes, and L. J. Kangas, “Three neural network based, sensor systems for environmental monitoring,” in Proceedings of the Electro/94 International Conference, pp. 377–382, Boston, Mass, USA, May 1994.
View at: Google Scholar
W. Bourgeois and R. M. Stuetz, “Measuring wastewater quality using a sensor array: prospects for real-time monitoring,” Water Science and Technology, vol. 41, no. 12, pp. 107–112, 2000.
View at: Google Scholar
R. E. Baby, M. Cabezas, and E. N. Walsöe De Reca, “Electronic nose: a useful tool for monitoring environmental contamination,” Sensors and Actuators B, vol. 69, no. 3, pp. 214–218, 2000.
View at: Publisher Site | Google Scholar
P. Wamane and D. C. Gharpure, “Detection of perfumes using gas sensors and artificial neural networks,” Journal of the Instrument Society of India, vol. 30, pp. 284–290, 2000.
View at: Google Scholar
B. Karlık and Y. Bastaki, “Real time monitoring odor sensing system using OMX-GR sensor and neural network,” WSEAS Transactions on Electronics, vol. 1, no. 2, pp. 337–342, 2004.
View at: Google Scholar
A. Iversen, N. K. Taylor, and K. E. Brown, “Classification and verification through the combination of the multi-layer perceptron and auto-association neural networks,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '05), vol. 2, pp. 1166–1171, Montreal, Quebec, Canada, July-August 2005.
View at: Publisher Site | Google Scholar
Z. Boger, D. C. Meier, R. E. Cavicchi, and S. Semancik, “Rapid identification of CW agents by artificial neural networks pruning of temperature programmed microsensor databases,” Sensor Letters, vol. 1, no. 1, pp. 86–92, 2003.
View at: Publisher Site | Google Scholar
B. Kusumoputro, A. Saptawijaya, and A. Murni, “Comparison of hybrid neural systems of KSOM-BP learning in artificial odor recognition system,” in Proceedings of the 4th International Conference on Computational Intelligence and Multimedia Applications (ICCIMA '01), pp. 276–281, Yokosuka City, Japan, October-November 2001.
View at: Publisher Site | Google Scholar
R. Dutta, E. L. Hines, J. L. Gardner, and P. Boilot, “Bacteria classification using cyranose 320 elcetronic nose,” BioMedical Engineering Online, vol. 1, no. 4, 2002.
View at: Publisher Site | Google Scholar
B. Karlık and Y. Bastaki, “Bad breathe diagnosis system using OMX-GR sensor and neural network for telemedicine,” Clinical Informatics and Telemedicine, vol. 2, pp. 237–239, 2004.
View at: Google Scholar
T. Temel and B. Karlık, “An improved odor recognition system using learning vector quantization with a new discriminant analysis,” in Proceedings of the 1st International Conference on Digital Communications and Computer Applications (DCCA '07), pp. 1075–1080, Amman, Jordan, March 2007.
View at: Google Scholar
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning Internal Representations by Error Propagation, MIT Press, Cambridge, Mass, USA, 1986.
View at: Google Scholar
Y. I. Lee, S.-H. Oh, H. K. Song, and M. W. Kim, “Design rules of multilayer perceptrons,” in Science of Artificial Neural Networks, vol. 1710 of Proceedings of SPIE, pp. 329–339, Orlando, Fla, USA, April 1992.
View at: Publisher Site | Google Scholar
Y. Ozbay and B. Karlık, “A fast training back-propagation algorithm on windows,” in Proceedings of the 3rd International Symposium on Mathematical & Computational Applications (ICMCA '02), pp. 204–210, Konya, Turkey, September 2002.
View at: Google Scholar
B. Karlık, “The effects of fuzzy clustering on the back-propagation algorithm,” in International Conference on Computational and Applied Mathematics, pp. 9–10, Kiev, Ukraine, September 2002.
View at: Google Scholar
B. Karlık, H. Tropi, and M. Alcı, “A fuzzy-neural approach for the characterization of the active microwave devices,” in Proccedings of the 12th International Conference on Microwave and Telecommunication Technology (CriMiCo '02), pp. 114–117, Sevastopol, Ukraine, September 2002.
View at: Google Scholar
B. Karlık, M. O. Tokhi, and M. Alcı, “A fuzzy clustering neural network architecture for multifunction upper-limb prosthesis,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 11, pp. 1255–1261, 2003.
View at: Publisher Site | Google Scholar
Y. Özbay, R. Ceylan, and B. Karlık, “A fuzzy clustering neural network architecture for classification of ECG arrhythmias,” Computers in Biology and Medicine, vol. 36, no. 4, pp. 376–388, 2006.
View at: Publisher Site | Google Scholar
J.-S. R. Jang, C.-T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prenctice-Hall, Upper Saddle River, NJ, USA, 1997.
View at: Google Scholar

Copyright

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.

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