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CLC number: TP183

On-line Access: 2010-11-08

Received: 2009-10-31

Revision Accepted: 2010-07-27

Crosschecked: 2010-10-12

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Journal of Zhejiang University SCIENCE A 2010 Vol.11 No.11 P.841-848

10.1631/jzus.A0900664


Memetic algorithms-based neural network learning for basic oxygen furnace endpoint prediction


Author(s):  Peng Chen, Yong-zai Lu

Affiliation(s):  Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China, Department of Automation, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   pengchen@sjtu.edu.cn, y.lu@ieee.org

Key Words:  Memetic algorithm (MA), Neural network (NN) learning, Back propagation (BP), Extremal optimization (EO), Levenberg-Marquardt (LM) gradient search, Basic oxygen furnace (BOF)


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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.

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}

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T1 - Memetic algorithms-based neural network learning for basic oxygen furnace endpoint prediction
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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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