Artificial Neural Networks Modeling of Release of Olanzapine From Glycerol Monooleate Matrices

Abstract

The important aim of this research was to develop an appropriate model to predict relationships between three casual factors on the responses based on an artificial neural network (ANN). As model formulation, 28 types of gels were prepared. The weight ratio of GMO/water (w/w) and PEG 300/GMO (w/w), percentage of Olanzapine (OZ) were selected as input data. Entrapment efficacy, maximum percentage of release, particle size and viscosity were estimated as gel characterization. A set of gel characterization and input data were employed as tutorial data for ANN methodology by using neural network toolbox in Matlab. Different topologies have been performed in order to determine the single network with good performance and accuracy. Four training algorithms (Levenberg–Marquardt, Bayesian- Regularization, BFGS Quasi-Newton, and Gradient Descent) were applied to train ANNs containing different numbers of hidden layers with various nods. The ability to predict the responses of all the algorithms were in the order of: BR > LM >BFGS> GD.