Optimized probabilistic neural networks in recognizing fragrance mixtures using higher number of sensors

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)


The electronic odor discrimination system have developed. The developed system showed high recognition probability to discriminate various single odors to its high generality properties; however, the system had a limitation in recognizing the fragrances mixture. 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. In this experiment, the improvement is conducted not only by replacing the last hardware system from 4 quartz resonator-basic resonance frequencies 10 MHz with new 16 quartz resonator-basic resonance frequencies 20 MHz, but also by replacing the pattern classifier from Back Propagation (BP) neural network with Variance of Back Propagation, Probabilistic Neural Network (PNN) and Optimized-PNN. The purpose of the recent study is to construct a new artificial odor discrimination system for recognizing fragrance mixtures. The using of new sensing system and employ various neural networks have produced higher capability to recognize the fragrance mixtures compared to the earlier mentioned system.

Original languageEnglish
Title of host publicationProceedings of the Fourth IEEE Conference on Sensors 2005
Number of pages4
Publication statusPublished - 2005
EventFourth IEEE Conference on Sensors 2005 - Irvine, CA, United States
Duration: 31 Oct 20053 Nov 2005

Publication series

NameProceedings of IEEE Sensors


ConferenceFourth IEEE Conference on Sensors 2005
Country/TerritoryUnited States
CityIrvine, CA


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