This paper presents an automatic system of neural networks (NNs) that has the ability to simulate and predict many of applied
problems. The system architectures are automatically reorganized and the experimental process starts again, if the required
performance is not reached. This processing is continued until the performance obtained. This system is first applied and tested
on the two spiral problem; it shows that excellent generalization performance obtained by classifying all points of the two-spirals
correctly. After that, it is applied and tested on the shear stress and the pressure drop problem across the short orifice die as a
function of shear rate at different mean pressures for linear low-density polyethylene copolymer (LLDPE) at 190◦C. The system
shows a better agreement with an experimental data of the two cases: shear stress and pressure drop. The proposed systemhas been
also designed to simulate other distributions not presented in the training set (predicted) and matched them effectively. |
