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Application of different training methodologies for the development of a back propagation artificial neural network retention model in ion chromatography
Author(s) -
Bolanča Tomislav,
CerjanStefanović Štefica,
Ukić Šime,
Rogošić Marko,
Luša Melita
Publication year - 2008
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1096
Subject(s) - artificial neural network , chlorate , ion chromatography , computer science , chemistry , reliability (semiconductor) , fluoride , biological system , chromatography , artificial intelligence , inorganic chemistry , power (physics) , physics , quantum mechanics , biology
The reliability of predicted separations in ion chromatography depends mainly on the accuracy of retention predictions. Any model able to improve this accuracy will yield predicted optimal separations closer to the reality. In this work artificial neural networks were used for retention modeling of void peak, fluoride, chlorite, chloride, chlorate, nitrate and sulfate. In order to increase performance characteristics of the developed model, different training methodologies were applied and discussed. Furthermore, the number of neurons in hidden layer, activation function and number of experimental data used for building the model were optimized in terms of decreasing the experimental effort without disruption of performance characteristics. This resulted in the superior predictive ability of developed retention model (average of relative error is 0.4533%). Copyright © 2008 John Wiley & Sons, Ltd.