Artificial odor discrimination system using multiple qartz resonator sensors and various neural networks for recognizing fragrance mixtures

IEEE Sensors Journal

Volumn 6, Issue 1, 2006, Pages 223-233

  Jatmiko, Wisnu ^a,b   Fukuda, Toshio ^a,b   Arai, Fumihito ^a,b   Kusumoputro, Benyamin ^c,d  

^a IEEE   (Japan)

^b NAGOYA UNIVERSITY   (Japan)

^c UNIVERSITY OF INDONESIA   (Indonesia)

^d Computational Intelligence Research Laboratory ^*

Author keywords

Fuzzy neuro learning vector quantization (FNLVQ); Matrix similarity analysis (MSA); Multiple quartz resonator sensors; Neural networks (NNs); Odor discrimination system

Indexed keywords

FUZZU-NEURO LEARNING VECTOR QUANTIZATION (FNLVQ); MATRIX SIMILARITY ANALYSIS (MSA); MULTIPLE QUARTZ-RESONATOR SENSOR; ODOR DISCRIMINATION SYSTEM;

FUZZY SETS; NEURAL NETWORKS; ODORS; PATTERN RECOGNITION; QUARTZ; RESONATORS; VECTOR QUANTIZATION;

SENSORS;

EID: 31144438167     PISSN: 1530437X     EISSN: None     Source Type: Journal    
DOI: 10.1109/JSEN.2005.858435     Document Type: Article

References (37)

1. T. C. Pearce, S. S. Schiffman, H. T. Nagle, and J. W. Gardner, Handbook of Machine Olfaction: Electronic Nose Technology. Weinheim, Germany: Wiley VCH, 2002.
2. J. W. Gardner and P. N. Bartlett, Electronic Nose: Principles and Applications. New York: Oxford Univ. Press, 1999.
3. J. Ide, T. Nakamoto, and T. Moriizumi, "Discrimination of aromatic optical isomers using quartz-resonator sensor," Sens. Actuators A, vol. 49, pp. 73-78, Jun. 1995.
4. R. Gutierrez-Osuna, "Pattern analysis for machine olfaction: A review," IEEE Sensors J., vol. 2, no. 3, pp. 189-202, Jun. 2002.
5. A. Perera et al., "A portable electronic nose based on embedded PC technology and GNU/Linux: Hardware, software and applications," IEEE Sensors J., vol. 2, no. 3, pp. 235-246, Jun. 2002.
6. P. E. Keller, "Physiologically inspired pattern recognition for electronic noses," Proc. SPIE, vol. 3722, no. 13, pp. 144-153, 1999.
7. _, "Overview of electronic nose algorithms," presented at the IJCNN, Washinton, DC, 1999.
8. B. Kusumoputro and M. Rivai et al., "Discrimination of fragrance odor by arrayed quartz resonator and a neural network," in Computer Intelligence and Multimedia Application, H. Selvaraj et al., Eds, Singapore: World Scientific, 1998, pp. 264-269.
9. W. Jatmiko, T. Fukuda, F. Arai, and B. Kusumoputro, "Artificial odor discrimination system for recognizing the fragance mixtures using multiple quartz-resonator sensors," in Proc. IEEE MHS, Nagoya, Japan, Oct. 2004, pp. 169-174.
10. A. M. Taurino, C. Distante, P. Siciliano, and L. Vasanelli, "Quantitave and qualitative analysis of VOC's mixtures by means of a microsensor array and different evaluation methods," Sens. Actuators B, vol. 93, pp. 117-125, Aug. 2003.
11. G. Daqi, W. Shuyan, and J. Yan, "An electronic nose and modular radial basis function network classifer for recognition multiple fragrant materials," Sens. Actuators B, vol. 97, pp. 391-401, Feb. 2004.
12. A. K. Srivastava, "Detection of volatile organic compound (VOC's) using SnO2 gas-sensor array and artificial neural network," Sens. Actuators B, vol. 96, pp. 24-37, Nov. 2003.
13. B. Kusumoputro, H. Budiarto, and W. Jatmiko, "Fuzzy-Neural LVQ and its comparison with fuzzy algorithm LVQ in artificial odor discrimination system," ISA Trans. Sci. Eng, Meas. Autom., vol. 31, pp. 395-407, Oct. 2002.
14. W. Jatmiko and B. Kusumoputro, "Using fuzzy-LVQ algorithm to recognize the unknown odor mixture in the artificial odor discrimination system," presented at the Int. Conf. Fundamentals of Electronics, Communication, and Computer Sciences, Tokyo, Japan, Mar. 2002.
15. B. Kusumoputro and W. Jatmiko, "Recognition of odor mixture using fuzzy-LVQ neural networks with matrix similarity analysis," presented at the IEEE APCASS, Oct. 2002.
16. G. Sauerbrey, "Vermendung von schwingquaren zur wagung dunner schichten und zur wagung," Z. Phys., vol. 155, pp. 206-209, 1959.
17. T. Masters, Advanced Algorithms for Neural Network. New York: Wiley, 1995.
18. S.Haykin, Neural Network, a Comprehensive Foundation, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 1999.
19. 2 gas sensing array for combustible and explosive gas leakage recognition," IEEE Sensors J., vol. 2, no. 3, pp. 140-149, Jun. 2002.
20. M. Pardo and G. Sberveglieri, "Remarks on the use of multilayer perceptron for the analysis of chemical sensor array data," IEEE Sensors J., vol. 4, no. 3, pp. 355-365, Jun. 2004.
21. H. Zhang, M. O. Balaban, and J. C. Principe, "Improving pattern recognition of electronic nose data with time-delay neural networks," Sens. Actuators B, vol. 96, pp. 385-389, Nov. 2003.
22. B. Kusumoputro, L. Rostiviany, and A. Saptawijaya, "Self-organized network with supervised training and its comparison with FALVQ in artificial odor recognition system," Proc. SPIE, vol. 4036, pp. 85-90, 2000.
23. B. Kusumoputro, P. Irwanto, and W. Jatmiko, "Optimization of fuzzy-neural structure through genetic algorithms and its application in artificial recognition system," in Proc. IEEE APCCAS, Oct. 2002, pp. 47-52.
24. M. Penza and G. Cassano, "Application of principal component analysis and artificial neural networks to recognize the individual VOC's of methanol/2-ptopanaol in a binary mixture by SAW multi-sensor array," Sens. Actuators B, vol. 89, pp. 269-284, Apr. 2003.
25. J. W. Gardner, P. Boilot, and E. L. Hines, "Enhancing Electronic Nose Performance by Sensor Selection Using a New Integer-Based Genetic Algorithm Approach," Sens. Actuators, vol. 97, pp. 114-121, Apr. 2004.
26. T. Kohonen, "Improved vesion of learning vector quantization," in Proc. IEEE IJCNN, vol. 1, 1990, pp. 545-550.
27. T. M. Martinetz, S. G. Berkovich, and K. J. Schulten, "Neural-gas network for vector quantization and its application to time-series prediction," IEEE Trans. Neural Netw., vol. 4, no. 4, pp. 558-569, Aug. 1993.
28. J. C. Bedzek and N. R. Pal, "Two soft relatives of learning vector quantization," IEEE Trans. Neural Netw., vol. 8, no. Oct., pp. 729-743, 1995.
29. J. C. Bedzek, "Integration and generalization of LVQ and c-means clustering," Proc. SPIE, vol. 1826, pp. 280-299, 1992.
30. N. B. Karayiannis and J. C. Bedzek, "An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering," IEEE Trans. Fuzzy Syst., vol. 5, no. 4, pp. 622-628, Aug. 1997.
31. N. B. Karayiannis, "Learning vector quantization: A review," J. Smart Eng. Syst. Design, vol. 1, pp. 33-58, 1997.
32. J. C. Bezdek, Pattern Recognition With Fuzzy Objective Function Algorithms. New York: Plenum, 1981.
33. R. J. Hathaway and J. C. Bezdek, "Optimization of clustering criteria by reformulation," IEEE Trans. Fuzzy Syst., vol. 3, no. 2, pp. 241-246, Apr. 1995.
34. N. B. Karayiannis and P. I. Pai et al., "A family of fuzzy algorithms for learning vector quantization," in Intelligent Engineering Systems through Artificial Neural Networks, C. H. Dagli et al., Eds. New York: ASME, 1994, pp. 219-224.
35. _, "Fuzzy algorithms for learning vector quantization," IEEE Trans. Neural Netw., vol. 7, no. 5, pp. 1196-1211, Oct. 1996.
36. _, "A fuzzy algorithm for learning vector quantization," in Proc. IEEE Int. Conf. Systems, Man, and Cybernetics, 1994, pp. 126-131.
37. Y. Sakuraba, T. Nakamoto, and T. Moriizumi, "New method of learning vector quantization," Syst. Comput. Jpn., vol. 22, no. 13, pp. 93-102, 1991.