Iterative Bayesian fuzzy clustering toward flexible icon-based assistive software for the disabled
Information Sciences: an International Journal
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Artificial odor recognition system is developed in order to mimic the human sensory test in cosmetics, perfume and beverage industries. Back-propagation neural network is used as the pattern recognition system and shows high recognition capability, however, the system work efficiently when it is only used to discriminate a limited number of odors. The unlearned odor will be forced to classify as one of the already learned category. To improve the systems capability, a fuzzy learning vector quantization neural network is developed, and utilized in the experiments on four different ethanol concentrations and three different kinds of fragrance odor from Martha Tilaar Cosmetics. The result shows that the FLVQ has a comparable ability for recognizing the already known-category of odors, however, the FLVQ algorithm can clustered the unknown-odor, to a different new class of odor.