CLC number: TP183
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2010-10-12
Cited: 3
Clicked: 6702
Peng Chen, Yong-zai Lu. Memetic algorithms-based neural network learning for basic oxygen furnace endpoint prediction[J]. Journal of Zhejiang University Science A, 2010, 11(11): 841-848.
@article{title="Memetic algorithms-based neural network learning for basic oxygen furnace endpoint prediction",
author="Peng Chen, Yong-zai Lu",
journal="Journal of Zhejiang University Science A",
volume="11",
number="11",
pages="841-848",
year="2010",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0900664"
}
%0 Journal Article
%T Memetic algorithms-based neural network learning for basic oxygen furnace endpoint prediction
%A Peng Chen
%A Yong-zai Lu
%J Journal of Zhejiang University SCIENCE A
%V 11
%N 11
%P 841-848
%@ 1673-565X
%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0900664
TY - JOUR
T1 - Memetic algorithms-based neural network learning for basic oxygen furnace endpoint prediction
A1 - Peng Chen
A1 - Yong-zai Lu
J0 - Journal of Zhejiang University Science A
VL - 11
IS - 11
SP - 841
EP - 848
%@ 1673-565X
Y1 - 2010
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A0900664
Abstract: Based on the critical position of the endpoint quality prediction for basic oxygen furnaces (BOFs) in steelmaking, and the latest results in computational intelligence (CI), this paper deals with the development of a novel memetic algorithm (MA) for neural network (NN) learning. Included in this is the integration of extremal optimization (EO) and levenberg-Marquardt (LM) gradient search, and its application in BOF endpoint quality prediction. The fundamental analysis reveals that the proposed EO-LM algorithm may provide superior performance in generalization, computation efficiency, and avoid local minima, compared to traditional NN learning methods. Experimental results with production-scale BOF data show that the proposed method can effectively improve the NN model for BOF endpoint quality prediction.
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