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Alternative TitlePrediction method of furnace temperature based on transfer learning and knowledge distillation
翟乃举1,2,3,4; 周晓锋1,2,3; 李帅1,2,3,4; 史海波1,2,3
Source Publication计算机集成制造系统
Contribution Rank1
Funding Organization辽宁省“兴辽英才计划”资助项目(XLYC1808009)
Keyword加热炉 迁移学习 时间卷积网络 知识蒸馏


Other Abstract

It is the core problem of combustion device optimization control in metallurgical enterprises to realize the accurate prediction of furnace temperature so as to use accurate control strategy to optimize the combustion control of heating furnace. At present, few researches focus on the temperature prediction of all heating zones and the applicability of neural network in furnace temperature prediction. To solve this problem, a prediction method of furnace temperature based on transfer learning and knowledge distillation is proposed. Firstly, the temperature prediction model of the source zone based on the temporal convolution network is established, and then the model transfer is completed by using the generative adversarial loss for domain adaptation, so as to realize the accurate prediction of the temperature in all heating zones. A distillation network based on multi-task learning is further established, which solves the shortcomings of the high delay of the deep transfer network by the way of teacher assisting student. The experimental results show that the proposed transfer learning network can significantly improve the accuracy of furnace temperature prediction, distillation network can significantly reduce network parameters, greatly improve the timeliness of furnace temperature prediction.

Document Type期刊论文
Corresponding Author周晓锋
Recommended Citation
GB/T 7714
翟乃举,周晓锋,李帅,等. 基于迁移学习和知识蒸馏的加热炉温度预测方法[J]. 计算机集成制造系统,2021:1-13.
APA 翟乃举,周晓锋,李帅,&史海波.(2021).基于迁移学习和知识蒸馏的加热炉温度预测方法.计算机集成制造系统,1-13.
MLA 翟乃举,et al."基于迁移学习和知识蒸馏的加热炉温度预测方法".计算机集成制造系统 (2021):1-13.
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