EFFECT OF NEURAL-NETWORK TOPOLOGY AND TRAINING END-POINT IN MODELING THE FLUIDIZED-BED GRANULATION PROCESS

The effect of the topology and the training end point of artificial neural networks (ANN) in the modelling of a fluidized bed granulation process is presented. The neural network topologies were designed on the basis of an earlier study (Murtoniemi et al., Int. J. Pharm., 108 (1994) 155-163). In the first part of this study, the networks contained only one hidden layer in which the number of neurons was either 10, 15, 20 or 25. The training end points with all four networks ranged from 0.15 to 0.07, with a step length of 0.01. In the second part, the training end point was fixed to be 0.12, while the number of neurons in the hidden layer varied from 10 to 25. The main purpose of this study was to find a suitable ANN in regard to the generalization ability and to compare the results to those calculated on the basis of multilinear stepwise regression analysis. The results showed that the number of hidden layer neurons did not affect the generalization ability of the networks and a proper generalization ability was achieved with rather simple networks. The training end point, however, had a significant effect on the generalization ability and it also affects the number of iteration epochs needed. In complicated systems this probably will affect remarkably the time required for the training.