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Indexed by:期刊论文

Date of Publication:2014-06-01

Journal:APPLIED SOFT COMPUTING

Included Journals:SCIE、EI

Volume:19

Page Number:430-437

ISSN No.:1568-4946

Key Words:Prediction model; Extreme learning machine; Evolutionary membrane algorithm; Soft measurement; Basic oxygen furnace; Endpoint carbon content; Endpoint temperature

Abstract:The endpoint parameters of molten steel, such as the steel temperature and the carbon content, directly affect the quality of the production steel. Moreover, these endpoint results cannot be the online continuous measurement in time. To solve the above-mentioned problems, an anti-jamming endpoint prediction model is proposed to predict the endpoint parameters of molten steel. More specifically, the model is constructed on the parameters of extreme learning machine (ELM) adaptively adjusted by the evolutionary membrane algorithm with the global optimization ability. In other words, the evolutionary membrane algorithm may find the suitable parameters of an ELM model which reduces the incidence of the over-fitting of ELM affected by the noise in the actual data. Finally, the proposed model is applied to predict the endpoint parameters of molten steel in steel-making. In the simulation experiments, two test problems, including 'SinC' function with the Gaussian noise and the actual production data of basic oxygen furnace (BOF) steel-making, are employed to evaluate the performance of the proposed model. The results indicate that the proposed model has good prediction accuracy and robustness in the data with noise. Therefore, the proposed model has good application prospects in the industrial field. (C) 2013 Elsevier B.V. All rights reserved.