| Title |
A Neural Net Type Process Model for Enhancing Learning Compensation Function in Hot Strip Finishing Rolling Mill |
| Authors |
Seong-Cheol Hong ; Hai-young Lee |
| DOI |
http://dx.doi.org/10.5207/JIEIE.2013.27.6.059 |
| Keywords |
Process Model ; Neural Net ; Finishing Rolling Mill ; Learning Compensation Function |
| Abstract |
This paper presents a neural net type process model for enhancing learning compensation function in hot strip finishing rolling mill. Adequate input and output variables of process model are chosen, the proposed model was designed as single layer neural net. Equivalent carbon content, strip thickness and rolling speed are suggested as input variables, and looper's manipulation variable is proposed as output variable. According to simulation result using process data to show the validity of the proposed process model, neural net type process model's outputs give almost similar data to process output under same input conditions. |