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Proceedings Paper

Fuzzy learning vector quantization neural network and its application for artificial odor recognition system
Author(s): Benyamin Kusumoputro; Hary Budiarto; Wisnu Jatmiko
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Paper Abstract

In this paper, a kind of fuzzy algorithm for learning vector quantization is developed and used as pattern classifiers with a supervised learning paradigm in artificial odor discrimination system. In this type of FLVQ, the neuron activation is derived through fuzziness of the input data, so that the neural system could deal with the statistical of the measurement error directly. During learning,the similarity between the training vector and the reference vectors are calculated, and the winning reference vector is updated in two ways. Firstly, by shifting the central position of the fuzzy reference vector toward or away from the input vector, and secondly, by modifying its fuzziness. Two types of fuzziness modifications are used, i.e., a constant modification factor and a variable modification factor. This type of FLVQ is different in nature with FALVQ, and in this paper, the performance of FNLVQ network is compared with that of FALVQ in artificial odor recognition system. Experimental results show that both FALVQ and FNLVQ provided high recognition probability in determining various learn-category of odors, however, the FNLVQ neural system has the ability to recognize the unlearn-category of odor that could not recognized by FALVQ neural system.

Paper Details

Date Published: 30 March 2000
PDF: 8 pages
Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000); doi: 10.1117/12.380590
Show Author Affiliations
Benyamin Kusumoputro, Univ. of Indonesia (Indonesia)
Hary Budiarto, Univ. of Indonesia (Indonesia)
Wisnu Jatmiko, Univ. of Indonesia (Indonesia)

Published in SPIE Proceedings Vol. 4055:
Applications and Science of Computational Intelligence III
Kevin L. Priddy; Paul E. Keller; David B. Fogel, Editor(s)

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