Visualization and statistical analysis of fuzzy-neuro learning vector quantization based on particle swarm optimization for recognizing mixture odors

An electronic nose system had been developed by using 16 quartz resonator sensitive membranes-basic resonance frequencies 20 MHz as a sensor, and analyzed the measurement data through various neural network as a pattern recognition system. The developed system showed high recognition probability to discriminate various single odors even mixture odor to its high generality properties; however the system still need improvement. In order to improve the performance of the proposed system, development of the sensor and other neural network are being sought. This paper explains the improvement of the capability of that system from the point of neural network system. It has been proved from our previous work that FLVQ (fuzzy learning vector quantization) which is LVQ (learning vector quantization) together with fuzzy theory shows high recognition capability compared with other neural networks, however FLVQ have a weakness for selecting the best codebook vector that will influence the result of recognition. This problem will be anticipated by adding the PSO (particle swarm optimization) method to select the best codebook vector. Then experiment show that the new recognition system (FLVQ-PSO) has produced higher capability compared to the earlier mentioned system.