An adaptive approach to improve the accuracy of a rolling load prediction model for a plate rolling process

We present a method that integrates off-line rule identification and an on- line adaptive approach to improve the accuracy of a rolling load prediction model for a plate rolling process. Based on the physical model of a plate rolling process, this work presents an empirical and adaptive approach to i mprove the accuracy of a rolling load prediction model. Our method consists of an off line rule identification method and an on-line adaptive method. Using a hierarchical clustering method, our rule identification method find s a set of optimal rules that determine appropriate model parameters depend ing on an operational environment. In contrast to traditional approaches wh ere such rules are determined in an ad-hoc manner, our method provides a "s ystematic" method to find optimal rules under the specification on model ac curacy. Then, using a recursive least-square error method, our on-line adap tive method tunes model parameters by feeding back the observed model error s. Our off-line approach is effective to deal with nonlinear characteristic s of the process, and our adaptive approach guarantees to maximize and to m aintain the accuracy even if time passes. A successful application of the p roposed approach to the plate rolling process is also shown.