NDLI: Application of variable-metric chaos optimization neural network in predicting slab surface temperature of the continuous casting

Content Provider	IEEE Xplore Digital Library
Author	Fengxiang Gao Changsong Wang Yubao Zhang Xiao Chen
Copyright Year	2009
Description	Author affiliation: Anyang Iron and Steel Company, 455004, China (Xiao Chen) \|\| Mechatronic Engineering Department, University of Science and Technology Beijing, 100083, China (Fengxiang Gao; Changsong Wang; Yubao Zhang)
Abstract	A slab surface temperature prediction model of the continuous casting based on the variable-metric chaos optimization neural network is presented to solve the problem which the slab surface temperatures can not be measured continuously directly for plentiful inhalator, water film and ferric oxide on the slab surface in the secondary cooling zone. The model is shown to fit the actual data precisely and to overcome several disadvantages of the conventional BP neural networks, namely: slow convergence, low accuracy and difficulty in finding the global optimum. A series of tests have been conducted based on the inputs of the continuous casting in a steel factory. It has been shown that the error is less than 1% between the predicted surface temperatures with the model and the actual temperatures, and the error is less than 2% between the predicted slab thicknesses with the model and the actual slab thicknesses. The model has yielded highly desirable results.
Starting Page	2296
Ending Page	2299
File Size	98720
Page Count	4
File Format	PDF
ISBN	9781424427222
DOI	10.1109/CCDC.2009.5192776
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Chaos Temperature Cooling Predictive models neural network Convergence slab surface temperature prediction Casting variable-metric chaos optimization Neural networks Surface fitting Slabs Testing
Content Type	Text
Resource Type	Article

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