Model identification theory using neural network and its application in plate rolling control

A method of identifying and modifying plate rolling model parameters on-line with model identification theory using neural network is introduced. Models of rolling force and of temperature were first analyzed to get suitable function styles for identification and modification with neural networks, and several neural network training algorithms, including the one with the steepest gradient, RLS and conjugated gradient algorithm, were chosen and compared. Off-line and on-line computer emulation and applications were then realized. The results show that the use of neural network in plate rolling process control can greatly improve the precision of model prediction.