Mixed odors classification by Neural Network using Radial Basis Function

Akhmad Faqih, Bharasaka Krisnandhika, Benyamin Kusumo Putro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Odor research has become an attractive topic to be developed because of its potential application in advanced technologies. Thus, the classification of mixed odor becomes more important. But, classifying the mixed odors is difficult in getting high recognition. In this paper, we consider the three mixture of odor using 8 and 16 channels of sensor as main data and focusing on its classification method using neural network. The neural network used here are a Radial Basis Function Neural Network (RBFNN). K-mean clustering and Self-organizing map (SOM) are used for gaining the centers and standard deviations. We classify the three mixtures of odor into 18 and 12 classes for each data type. The result show high recognition which the accuracy rates using 16 channels of sensor are around 84.9% for 18 classes and around 93.4% for 12 classes, while the accuracy rates using 8 channels of sensor are around 88.2% for 18 classes and around 91.9% for 12 classes.

Original languageEnglish
Title of host publication2017 3rd International Conference on Control, Automation and Robotics, ICCAR 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages567-570
Number of pages4
ISBN (Electronic)9781509060870
DOIs
Publication statusPublished - 7 Jun 2017
Event3rd International Conference on Control, Automation and Robotics, ICCAR 2017 - Nagoya, Japan
Duration: 22 Apr 201724 Apr 2017

Publication series

Name2017 3rd International Conference on Control, Automation and Robotics, ICCAR 2017

Conference

Conference3rd International Conference on Control, Automation and Robotics, ICCAR 2017
CountryJapan
CityNagoya
Period22/04/1724/04/17

Keywords

  • Classification
  • Mixed odor
  • Neural network
  • Radial basis function

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