Forecasting the steel productivity of a cold rolling sizing unit with the radial basis function neural network

Author

Wang, Xudong ; Shao, Huihe

Author_Institution

Dept. of Autom., Shanghai Jiaotong Univ., China

Volume

2

fYear

1996

fDate

11-13 Dec 1996

Firstpage

1734

Abstract

The goods delivery forecasting system of an industrial process can shorten the stocking time of products so that the production cost becomes low. It includes several process models, so the key task of designing such a system is modeling. In this paper, the goods delivery forecasting system of a cold rolling system of a steel factory is studied and its steel productivity forecasting model is designed with the radial basis function (RBF) neural network. Such a model is based on the actual data of a cold rolling sizing unit. The results show that the RBF neural network based forecasting model of the steel productivity is effective

Keywords

cold rolling; computer aided production planning; feedforward neural nets; goods distribution; least squares approximations; production control; steel industry; stock control; cold rolling sizing unit; goods delivery forecasting system; radial basis function neural network; steel productivity; stocking time; Feedforward neural networks; Least squares approximation; Neural networks; Neurons; Predictive models; Production facilities; Production systems; Productivity; Radial basis function networks; Steel;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1996., Proceedings of the 35th IEEE Conference on

Conference_Location

Kobe

ISSN

0191-2216

Print_ISBN

0-7803-3590-2

Type

conf

DOI

10.1109/CDC.1996.572809

Filename

572809