Recognition of odor mixture using fuzzy-LVQ neural networks with matrix similarity analysis

Author

Kusumoputro, Benyamin ; Jatmiko, Wisnu

Author_Institution

Fac. of Comput. Sci., Univ. of Indonesia, Jakarta, Indonesia

Volume

2

fYear

2002

fDate

2002

Firstpage

57

Abstract

An artificial odor recognition system has been developed recently. However, recognizing the odor mixture is rather difficult by the use of a limited number of sensors. We have constructed an artificial odor recognition system based on 16 sensors of 20 MHz quartz resonators. Various neural systems, i.e. backpropagation neural system, probabilistic neural system and fuzzy-LVQ, are then studied and applied as the neural classifier of the developed system. Results of experiments confirmed that the F-LVQ shows higher recognition rate compared with that of two other neural systems. Improving the F-LVQ is then conducted by incorporating the matrix similarity analysis to form FLVQ-MSA, showing the highest average recognition rate of 80% on determining three-mixture odors.

Keywords

crystal resonators; fuzzy neural nets; gas sensors; learning (artificial intelligence); vector quantisation; 20 MHz; backpropagation neural system; fuzzy-LVQ neural networks; learning vector quantization; matrix similarity analysis; neural classifier; odor mixture recognition; quartz resonators; recognition rate; Artificial neural networks; Backpropagation; Circuits; Cost function; Frequency; Neural networks; Neurons; Pattern recognition; Sensor arrays; Sensor systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2002. APCCAS '02. 2002 Asia-Pacific Conference on

Print_ISBN

0-7803-7690-0

Type

conf

DOI

10.1109/APCCAS.2002.1115120

Filename

1115120