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Applied Computational Intelligence and Soft Computing
Volume 2009 (2009), Article ID 721370, 11 pages
http://dx.doi.org/10.1155/2009/721370
Research Article

Reorganizing Neural Network System for Two Spirals and Linear Low-Density Polyethylene Copolymer Problems

1Mathematics Department, Faculty of Science, Mansoura University, New Damietta, Egypt
2Physics Department, Faculty of Science, Benha University, Al Qalyubiyah, Egypt

Received 28 February 2009; Revised 24 October 2009; Accepted 12 November 2009

Academic Editor: Zhigang Zeng

Copyright © 2009 G. M. Behery et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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 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.