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Titolo:
An adaptive approach to improve the accuracy of a rolling load prediction model for a plate rolling process
Autore:
Nishino, S; Narazaki, H; Kitamura, A; Morimoto, Y; Ohe, K;
Indirizzi:
Kobe Steel Ltd, Proc Technol Res Lab, Nishi Ku, Kobe, Hyogo 6512271, JapanKobe Steel Ltd Kobe Hyogo Japan 6512271 hi Ku, Kobe, Hyogo 6512271, Japan Kobe Steel Ltd, Elect Technol Res Lab, Nishi Ku, Kobe, Hyogo 6512271, Japan Kobe Steel Ltd Kobe Hyogo Japan 6512271 hi Ku, Kobe, Hyogo 6512271, Japan Kobe Steel Ltd, Iron & Steel Co, Kakogawa Works, Res & Dev Labs, Kakogawa 6750023, Japan Kobe Steel Ltd Kakogawa Japan 6750023 Dev Labs, Kakogawa 6750023, Japan
Titolo Testata:
ISIJ INTERNATIONAL
fascicolo: 12, volume: 40, anno: 2000,
pagine: 1216 - 1222
SICI:
0915-1559(2000)40:12<1216:AAATIT>2.0.ZU;2-7
Fonte:
ISI
Lingua:
ENG
Keywords:
plate rolling; rolling load; identification; adaptive method; clustering; recursive least-square method;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
10
Recensione:
Indirizzi per estratti:
Indirizzo: Nishino, S Kobe Steel Ltd, Proc Technol Res Lab, Nishi Ku, Kobe, Hyogo 6512271, Japan Kobe Steel Ltd Kobe Hyogo Japan 6512271 , Hyogo 6512271, Japan
Citazione:
S. Nishino et al., "An adaptive approach to improve the accuracy of a rolling load prediction model for a plate rolling process", ISIJ INT, 40(12), 2000, pp. 1216-1222

Abstract

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 platerolling 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.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 09/04/20 alle ore 10:13:18