Recognizing Odor Mixtures Using Optimized Fuzzy Neural Network Through Genetic Algorithms | Zendy

Benyamin Kusumoputro | Zendy; Teguh P. Arsyad | Zendy

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Recognizing Odor Mixtures Using Optimized Fuzzy Neural Network Through Genetic Algorithms

Author(s) -

Benyamin Kusumoputro,

Teguh P. Arsyad

Publication year - 2005

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2005.p0290

Subject(s) - computer science , odor , artificial neural network , artificial intelligence , pattern recognition (psychology) , classifier (uml) , multilayer perceptron , neuro fuzzy , probabilistic neural network , fuzzy logic , genetic algorithm , probabilistic logic , time delay neural network , algorithm , machine learning , fuzzy control system , neuroscience , biology

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.

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