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 type of gel 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 topology has been performed in order to determine the one network with well 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 layer with various nods. The ability to predict the responses of the all algorithms were in the order of: BR > LM >BFGS> GD.
Artificial neural network, Multilayer perceptron, Training algorithms, Olanzapine, Glycerol monooleate