Locally weighted neural networks for an analysis of the biosensor response

Romas Baronas; Feliksas Ivanauskas; Romualdas Maslovskis; Marijus Radavičius; Pranas Vaitkus

Kybernetika (2007)

  • Volume: 43, Issue: 1, page 21-30
  • ISSN: 0023-5954

Abstract

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This paper presents a semi-global mathematical model for an analysis of a signal of amperometric biosensors. Artificial neural networks were applied to an analysis of the biosensor response to multi-component mixtures. A large amount of the learning and test data was synthesized using computer simulation of the biosensor response. The biosensor signal was analyzed with respect to the concentration of each component of the mixture. The paradigm of locally weighted linear regression was used for retraining the neural networks. The application of locally weighted regression significantly improved the quality of the prediction of the concentrations.

How to cite

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Baronas, Romas, et al. "Locally weighted neural networks for an analysis of the biosensor response." Kybernetika 43.1 (2007): 21-30. <http://eudml.org/doc/33837>.

@article{Baronas2007,
abstract = {This paper presents a semi-global mathematical model for an analysis of a signal of amperometric biosensors. Artificial neural networks were applied to an analysis of the biosensor response to multi-component mixtures. A large amount of the learning and test data was synthesized using computer simulation of the biosensor response. The biosensor signal was analyzed with respect to the concentration of each component of the mixture. The paradigm of locally weighted linear regression was used for retraining the neural networks. The application of locally weighted regression significantly improved the quality of the prediction of the concentrations.},
author = {Baronas, Romas, Ivanauskas, Feliksas, Maslovskis, Romualdas, Radavičius, Marijus, Vaitkus, Pranas},
journal = {Kybernetika},
keywords = {locally weighted regression; artificial neural network; physico-chemical change of biological sensing elements},
language = {eng},
number = {1},
pages = {21-30},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Locally weighted neural networks for an analysis of the biosensor response},
url = {http://eudml.org/doc/33837},
volume = {43},
year = {2007},
}

TY - JOUR
AU - Baronas, Romas
AU - Ivanauskas, Feliksas
AU - Maslovskis, Romualdas
AU - Radavičius, Marijus
AU - Vaitkus, Pranas
TI - Locally weighted neural networks for an analysis of the biosensor response
JO - Kybernetika
PY - 2007
PB - Institute of Information Theory and Automation AS CR
VL - 43
IS - 1
SP - 21
EP - 30
AB - This paper presents a semi-global mathematical model for an analysis of a signal of amperometric biosensors. Artificial neural networks were applied to an analysis of the biosensor response to multi-component mixtures. A large amount of the learning and test data was synthesized using computer simulation of the biosensor response. The biosensor signal was analyzed with respect to the concentration of each component of the mixture. The paradigm of locally weighted linear regression was used for retraining the neural networks. The application of locally weighted regression significantly improved the quality of the prediction of the concentrations.
LA - eng
KW - locally weighted regression; artificial neural network; physico-chemical change of biological sensing elements
UR - http://eudml.org/doc/33837
ER -

References

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