A hybird learning model for on-line prediction in hot skip-passing using neural networks

This paper presents a hybrid learning approach for dynamic system modelling and prediction using neural networks. The model learning is divided into two parts. One is to select the global region and the other is to find the goal value. A heuristic learning algorithm (HLA) is discussed, which is effective in the real-time dynamic modelling and control. The hybrid model is applied to the on-line prediction of the rolling strip in the hot skip-pass process. The control system is introduced and the result is discussed.