期刊名称：Applied Computational Intelligence and Soft Computing
印刷版ISSN：1687-9724
电子版ISSN：1687-9732
出版年度：2009
卷号：2009
DOI：10.1155/2009/721370
出版社：Hindawi Publishing Corporation
摘要：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 system has been also designed to simulate other distributions not presented in the training set (predicted) and matched them effectively.
