Application of adaptive RBF Networks to odour classification using conducting polymer sensor array

H G Byun; N Y Kim; K C Persaud; J S Huh; D D Lee

Application of adaptive RBF Networks to odour classification using conducting polymer sensor array

H G Byun, N Y Kim, K C Persaud, J S Huh, D D Lee

Research output: Chapter in Book/Conference proceeding › Chapter › peer-review

Abstract

This paper presents the application of adaptive RBF(Radial Basis Function) Networks to odour classification using conducting polymer sensor array. The performance of RBF Network is highly dependent on the choice of centers and widths in basis function. For the fine tuning of centers and widths, those are initialized by ill-conditioned genetic fuzzy c-means algorithm and the distribution of input patterns in the very first leaning stage, the stochastic gradient (SG) method is adapted. The adaptive RBF Network, which has tuned centers and width by SG method, has shown good classification performance for complex and mixture chemical patterns and confirmed by experimental trails.

Original language	English
Title of host publication	Electronic Noses and Olfaction 2000: Proceedings of the 7th International Symposium on Olfaction and Electronic Noses, Brighton, UK, July 2000
Publication status	Published - 2000

Cite this

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title = "Application of adaptive RBF Networks to odour classification using conducting polymer sensor array",

abstract = "This paper presents the application of adaptive RBF(Radial Basis Function) Networks to odour classification using conducting polymer sensor array. The performance of RBF Network is highly dependent on the choice of centers and widths in basis function. For the fine tuning of centers and widths, those are initialized by ill-conditioned genetic fuzzy c-means algorithm and the distribution of input patterns in the very first leaning stage, the stochastic gradient (SG) method is adapted. The adaptive RBF Network, which has tuned centers and width by SG method, has shown good classification performance for complex and mixture chemical patterns and confirmed by experimental trails.",

author = "Byun, {H G} and Kim, {N Y} and Persaud, {K C} and Huh, {J S} and Lee, {D D}",

note = "Times Cited: 1 Gardner, JW Persaud, KC 7th International Symposium on Olfaction and Electronic Noses (ISOEN 2000) JUL, 2000 BRIGHTON, ENGLAND",

year = "2000",

language = "English",

booktitle = "Electronic Noses and Olfaction 2000: Proceedings of the 7th International Symposium on Olfaction and Electronic Noses, Brighton, UK, July 2000",

}

Application of adaptive RBF Networks to odour classification using conducting polymer sensor array. / Byun, H G; Kim, N Y; Persaud, K C et al.
Electronic Noses and Olfaction 2000: Proceedings of the 7th International Symposium on Olfaction and Electronic Noses, Brighton, UK, July 2000. 2000.

Research output: Chapter in Book/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Application of adaptive RBF Networks to odour classification using conducting polymer sensor array

AU - Byun, H G

AU - Kim, N Y

AU - Persaud, K C

AU - Huh, J S

AU - Lee, D D

N1 - Times Cited: 1 Gardner, JW Persaud, KC 7th International Symposium on Olfaction and Electronic Noses (ISOEN 2000) JUL, 2000 BRIGHTON, ENGLAND

PY - 2000

Y1 - 2000

N2 - This paper presents the application of adaptive RBF(Radial Basis Function) Networks to odour classification using conducting polymer sensor array. The performance of RBF Network is highly dependent on the choice of centers and widths in basis function. For the fine tuning of centers and widths, those are initialized by ill-conditioned genetic fuzzy c-means algorithm and the distribution of input patterns in the very first leaning stage, the stochastic gradient (SG) method is adapted. The adaptive RBF Network, which has tuned centers and width by SG method, has shown good classification performance for complex and mixture chemical patterns and confirmed by experimental trails.

AB - This paper presents the application of adaptive RBF(Radial Basis Function) Networks to odour classification using conducting polymer sensor array. The performance of RBF Network is highly dependent on the choice of centers and widths in basis function. For the fine tuning of centers and widths, those are initialized by ill-conditioned genetic fuzzy c-means algorithm and the distribution of input patterns in the very first leaning stage, the stochastic gradient (SG) method is adapted. The adaptive RBF Network, which has tuned centers and width by SG method, has shown good classification performance for complex and mixture chemical patterns and confirmed by experimental trails.

M3 - Chapter

BT - Electronic Noses and Olfaction 2000: Proceedings of the 7th International Symposium on Olfaction and Electronic Noses, Brighton, UK, July 2000

ER -

Application of adaptive RBF Networks to odour classification using conducting polymer sensor array

Abstract

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