Recognizing Odor Mixtures Using Optimized Fuzzy Neural Network Through Genetic Algorithms
Benyamin Kusumoputro, and Teguh P. Arsyad
Faculty of Computer Science, University of Indonesia, Depok Campus, Jakarta Indonesia, PO.Box 3443 Jakarta 10002
Recognizing odor mixtures is rather difficult in artificial odor recognition system, especially when the number of sensors is limited. Classification is further hampered if the number of unlearned odor mixtures classes is increased. We developed a fuzzy-neuro multilayer perceptron as a pattern classifier and compared its recognition with that of the Probabilistic Neural Network and Back-propagation Neural Network. To enhance the recognition capability of the system, we then optimized fuzzy-neuro multilayer perceptron topology by deleting its weak weight connections using Genetic Algorithms. Experimental results show that the optimized fuzzy-neuro multilayer perceptron has the highest recognition in 18 classes of two-mixture odors with almost 98.2% when using hardware with 16 sensors, compared to 83.3% when using 8 sensors.