Pitfalls of artificial neural networks (ANN) modelling technique for data sets containing outlier measurements using a study on mixture properties ofa direct compressed dosage form

An application of the Artificial Neural Networks (ANN) methodology was inve stigated using experimental data from a mixture properties study and compar ed to classical modelling technique (i.e. Response Surface Methodology, RSM ) both graphically and numerically. The aim of this investigation was to qu antitatively describe the achieved degree of data fitting and robustness of the developed models. For comparing the goodness of fit, the R-2 coefficie nt was used, whereas for the robustness of the models an outlier measuremen t was integrated in the data set. Comparable results were achieved for both ANN- and RSM methodologies for data fitting. The robustness of the models towards outliers was clearly better for the RSM methodology. All determined mixture properties were mainly influenced by the concentration of silica a erogel, whereas the other factors showed very much lower effects. For that reason the physical properties of this excipient (e.g. its specific surface area) are of importance for the behaviour of the mixtures. (C) 1998 Elsevi er Science B.V. All rights reserved.