Artificial Neural Network ANN Models

The prediction power of theoretical modeling and an ANN model in IPC were compared. A feed-forward layered back-propagation ANN was used the input layer consisted of three neurons representing the fraction of organic modifier, the IPR, 

and Hartwick, R.A. Mechanism of ion-pair liquid chromatography of amines, neutrals, zwitterions and acids using anionic hetaerons. /. Chmmatogr. 1981, 204, 3-21. 

and Johnson, E.L. Practical aspects of reversed phase ion-pair chromatography. J. Chromatogr. Sci. 1977, 15, 413 23. 

Horvath, C. et al. Enhancement of retention hy ion-pair formation in liquid chromatography with nonpolar stationary phases. Anal. Chem. 1977,49, 2295-2305. 

Melander, W.R. and Horvath, C. Mechanistic stufy of ion-pair reversed-phase chromatography. J. Chmmatogr. 1980, 201, 211-224. 

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Bourquin J, Schmidli H, van Hoogevest P, Leuenberger H. Basic concepts of artificial neural networks (ANN) modelling in the application to pharmaceutical development. Pharm Dev Technol 1997 2 95-109. 

Degim T, Hadgraft J, Ilbasmis S, Ozkan Y (2003) Prediction of skin penetration using artificial neural network (ANN) modeling. J Pharm Sci 92 656-664. 

Yamamura S (2003) Clinical application of artificial neural network (ANN) modeling to predict pharmacokinetic parameters of severely ill patients. Adv Drug Deliv Rev 5 1233-1251. 

An artificial neural network (ANN) model was developed to predict the structure of the mesoporous materials based on the composition of their synthesis mixtures. The predictive ability of the networks was tested through comparison of the mesophase structures predicted by the model and those actually determined by XRD. Among the various ANN models available, three-layer feed-forward neural networks with one hidden layer are known to be universal approximators [11, 12]. The neural network retained in this work is described by the following set of equations that correlate the network output S (currently, the structure of the material) to the input variables U, which represent here the normalized composition of the synthesis mixture ... 

Bourquin, J., Schmidli, H., van Hoogevest, P., and Leuenberger, H. (1998), Pitfalls of artificial neural networks (ANN) modeling technique for data sets containing outher measurements using a study on mixture properties of a direct compressed dosage form, Eur. J. Pharm. Sci., 7,17-28. 

Q Development of Artificial Neural Network (ANN) Model for Estimation of Vapor Liquid Equilibrium (VLE) Data... 

Application areas for artificial neural network (ANN) models fit into one of the following four classes. 

As a nonlinear problem, predicting nonwoven properties fix)m the processing parameters and structural characteristics can be realized by an empirical modeling method that includes the statistical model, artificial neural network (ANN) model and others. ANN models have been shown to provide good approximations in presence of noisy data and smaller number of experimental points and the assumptions imder which ANN models work are less strict than those for statistical models [1]. 

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