An Adaptive Approach to Improve the Accuracy of a Rolling Load Prediction Model for a Plate Rolling Process
| Autor: | Yoshio Morimoto, Akira Kitamura, Satoshi Nishino, Ken-ichi Ohe, Hiroshi Narazaki |
|---|---|
| Rok vydání: | 2000 |
| Předmět: |
Engineering
Adaptive algorithm Computer simulation business.industry Mechanical Engineering Metals and Alloys Process (computing) Hierarchical clustering Nonlinear system Identification (information) Mechanics of Materials Control theory visual_art Materials Chemistry visual_art.visual_art_medium business Cluster analysis Sheet metal Simulation |
| Zdroj: | ISIJ International. 40:1216-1222 |
| ISSN: | 0915-1559 |
| DOI: | 10.2355/isijinternational.40.1216 |
| Popis: | 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 improve 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 finds a set of optimal rules that determine appropriate model parameters depending on an operational environment. In contrast to traditional approaches where such rules are determined in an ad-hoc manner, our method provides a systematic method to find optimal rules under the specification on model accuracy. Then, using a recursive least-square error method, our on-line adaptive method tunes model parameters by feeding back the observed model errors. Our off-line approach is effective to deal with nonlinear characteristics of the process, and our adaptive approach guarantees to maximize and to maintain the accuracy even if time passes. A successful application of the proposed approach to the plate rolling process is also shown. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|