Keywords: locally weighted regression; artificial neural network; modelling; biosensor
@article{KYB_2007_43_1_a1,
author = {Baronas, Romas and Ivanauskas, Feliksas and Maslovskis, Romualdas and Radavi\v{c}ius, Marijus and Vaitkus, Pranas},
title = {Locally weighted neural networks for an analysis of the biosensor response},
journal = {Kybernetika},
pages = {21--30},
year = {2007},
volume = {43},
number = {1},
mrnumber = {2343328},
zbl = {1136.62374},
language = {en},
url = {http://geodesic.mathdoc.fr/item/KYB_2007_43_1_a1/}
}
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 SP - 21 EP - 30 VL - 43 IS - 1 UR - http://geodesic.mathdoc.fr/item/KYB_2007_43_1_a1/ LA - en ID - KYB_2007_43_1_a1 ER -
%0 Journal Article %A Baronas, Romas %A Ivanauskas, Feliksas %A Maslovskis, Romualdas %A Radavičius, Marijus %A Vaitkus, Pranas %T Locally weighted neural networks for an analysis of the biosensor response %J Kybernetika %D 2007 %P 21-30 %V 43 %N 1 %U http://geodesic.mathdoc.fr/item/KYB_2007_43_1_a1/ %G en %F KYB_2007_43_1_a1
Baronas, Romas; Ivanauskas, Feliksas; Maslovskis, Romualdas; Radavičius, Marijus; Vaitkus, Pranas. Locally weighted neural networks for an analysis of the biosensor response. Kybernetika, Tome 43 (2007) no. 1, pp. 21-30. http://geodesic.mathdoc.fr/item/KYB_2007_43_1_a1/
[1] Artursson T., Eklöv T., Lundström I., Mårtensson P., Sjöström, M., Holmberg M.: Drift correction for gas sensors using multivariate methods. J. Chemometrics 14 (2000), 711–723 | DOI
[2] Atkeson C. G., Moore A. W., Schaal S.: Locally weighted learning. Artificial Intelligence Rev. 11 (1997), 11–73 | DOI
[3] Baronas R., Christensen J., Ivanauskas, F., Kulys J.: Computer simulation of amperometric biosensor response to mixtures of compounds. Nonlinear Anal. Model. Control 7 (2002), 3–14 | Zbl
[4] Baronas R., Ivanauskas, F., Kulys J.: The influence of the enzyme membrane thickness on the response of amperometric biosensors. Sensors 3 (2003), 248–262 | DOI
[5] Baronas R., Ivanauskas F., Maslovskis, R., Vaitkus P.: An analysis of mixtures using amperometric biosensors and artificial neural networks. J. Math. Chem. 36 (2004), 281–297 | DOI | MR | Zbl
[6] Chan L. W., Szeto C. C.: Training recurrent network with block-diagonal approximated Levenberg–Marquardt algorithm. In: Proc. IEEE Internat. Joint Conference on Neural Networks, IJCNN ’99, pp. 1521–1526, 1999
[7] Devroye L., Gyorfi, L., Lugosi G.: A Probabilistic Theory of Pattern Recognition. Springer–Verlag, New York 1996 | MR
[8] Haykin S.: Neural Networks: A Comprehensive Foundation. Second edition. Prentice Hall, New York 1999 | Zbl
[9] INTELLISENS: Intelligent Signal Processing of Biosensor Arrays Using Pattern Recognition for Characterisation of Wastewater: Aiming Towards Alarm Systems. EC RTD project. 2000 – 2003
[10] Malkavaara P., Alén, R., Kolehmainen E.: Chemometrics: an important tool for the modern chemist, an example from wood-processing chemistry. J. Chem. Inf. Comput. Sci. 40 (2000), 438–441 | DOI
[11] Martens H., Næs T.: Multivariate Calibration. Wiley, Chichester 1989 | MR | Zbl
[12] Moore A. W., Schneider J. G., Deng K.: Efficient Locally Weighted Polynomial Regression Predictions. In: Proc. Fourteenth International Conference on Machine Learning, pp. 236–244, 1997
[13] Nakamoto T., Hiramatsu H.: Study of odor recorder for dynamical change of odor using QCM sensors and neural network. Sens. Actuators B 85 (2002), 98–105 | DOI
[14] Patterson D.: Artificial Neural Networks, Theory and Applications. Prentice Hall, Upper Saddle River 1996 | Zbl
[15] Rao C. R.: Linear Statistical Inference and its Application. Wiley, New York 1973 | MR
[16] Rogers K. R.: Biosensors for environmental applications. Biosens. Biolectron. 10 (1995), 533–541 | DOI
[17] Ruppert D., Wand M. P.: Multivariate locally weighted least squares regression. Ann. Statist. 22 (1994), 1346–1370 | DOI | MR | Zbl
[18] Ruzicka J., Hansen E. H.: Flow Injection Analysis. Wiley, New York 1988
[19] Samarskii A. A.: The Theory of Difference Schemes. Marcel Dekker, New York – Basel 2001 | MR | Zbl
[20] Schaal S., Atkeson C. G.: Assessing the quality of learned local models, In: Advances in Neural Information Processing Systems 6 (J. Cowan, G. Tesauro, J. Alspector, eds.), Morgan Kaufmann 1994, pp. 160–167
[21] Schaal S., Atkeson C. G.: Constructive incremental learning from only local information. Neural Comput. 10 (1998), 2047–2084 | DOI
[22] Scheller F., Schubert F.: Biosensors, Vol. 7. Elsevier, Amsterdam 1992
[23] Schulmeister T.: Mathematical modelling of the dynamics of amperometric enzyme electrodes. Selective Electrode Rev. 12 (1990), 260–303
[24] Turner A. P. F., Karube, I., Wilson G. S.: Biosensors: Fundamentals and Applications. Oxford University Press, Oxford 1987
[25] Wang Z., Isaksson, T., Kowalski B. R.: New approach for distance measurement in locally weighted regression. Anal. Chem. 66 (1994), 249–260 | DOI
[26] Wollenberger U., Lisdat, F., Scheller F. W.: Frontiers in Biosensorics 2: Practical Applications. Birkhauser Verlag, Basel 1997
[27] Ziegler C., Göpel W., Hämmerle H., Hatt H., Jung G., Laxhuber L., Schmidt H.-L., Schütz S., Vögtle, F., Zell A.: Bioelectronic noses: A status report. Part II. Biosens. Bioelectron. 13 (1998), 539–571