Abstract
This paper presents the use of Evolving Fuzzy Neural Networks as pattern recognition system for odor recognition in an artificial nose. In the classification of gases derived from the petroliferous industry, the method presented achieves better results (mean classification error of 0.88%) than those obtained by Multi-Layer Perceptron (13.88%) and Time Delay Neural Networks (10.54%).
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References
Gardner, J.W., Hines, E.L.: Pattern Analysis Techniques. In: Handbook of Biosensors and Electronic Noses: Medicine, Food and the Environment, pp. 633–652. CRC Press, Boca Raton (1997)
Kasabov, N.: Evolving Fuzzy Neural Networks for Supervised/Unsupervised On-Line, Knowledge-Based Learning. IEEE Trans. on Systems, Man and Cybernetics, Part B: Cybernetics 31(6), 902–918 (2001)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Representations by Backpropagation Errors. Nature (323), 533–536 (1986)
Yamazaki, A., Ludermir, T.B.: Classification of Vintages of Wine by an Artificial Nose with Neural Networks. In: International Conference on Neural Information Processing, China, vol. 1, pp. 184–187 (2001)
Zanchettin, C., Ludermir, T.B.: Wavelet Filter for Noise Reduction and Signal Compression in an Artificial Nose. In: Hybrid intelligent System, Melbourne, Austrália, pp. 907–916 (2003)
Zanchettin, C., Ludermir, T.B.: A Neuro-Fuzzy Model Applied to Odor Recognition in an Artificial Nose. In: Hybrid intelligent System, Melbourne, Australia, pp. 917–926 (2003)
Kasabov, N.: The ECOS framework and the “eco” training method for evolving connectionist systems. Journal of Advanced Computational Intelligence 2(6), 1–8 (1983)
Persaud, K.C., Travers, P.J.: Arrays of Broad Specificity Films for Sensing Volatile Chemicals. In: Kress-Rogers, E. (ed.) Handbook of Biosensors and Electronic Noses: Medicine. Food and the Environment, pp. 563–592. CRC Press, Boca Raton (1997)
Prechelt, L.: Proben1 – A Set of Neural Network Benchmark Problems and Benchmarking Rules. Technical Report 21/94, Universität Karlsruhe, Germany (1994)
Fletcher, R.: Pratical Methods of Optimization. Wiley, Chichester (1987)
Johson, R.A., Wichern, D.W.: Applied Multivariate Statistical Analysis, 767 p. Prentice-Hall, Englewood Cliffs (1999)
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Zanchettin, C., Ludermir, T.B. (2004). Evolving Fuzzy Neural Networks Applied to Odor Recognition. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_147
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DOI: https://doi.org/10.1007/978-3-540-30499-9_147
Publisher Name: Springer, Berlin, Heidelberg
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