Fuzzy learning vector quantization based on particle swarm optimization for artificial odor dicrimination system

Wisnu Jatmiko, Rochmatullah, Benyamin Kusumo Putro, K. Sekiyama, T. Fukuda

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

Abstract

An electronic nose system had been developed by using 16 quartz resonator sensitive membranesbasic 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 previos work that FLVQ (Fuzzy Learning Vectoq 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 showt that the new recognition system (FLVQ-PSO) has produced higher capability compared to the earlier mentioned system.

Original languageEnglish
Pages (from-to)1239-1252
Number of pages14
JournalWSEAS Transactions on Systems
Volume8
Issue number12
Publication statusPublished - 1 Dec 2009

Keywords

  • Codebook
  • Electronic nose
  • Fuzzy learning vector quantization
  • Matrix similarity analysis
  • Odor
  • Particle swarm optimization

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