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

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