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Alternative TitleOnline Prediction Method for Generation and Consumption of Blast Furnace Gas Based on Adaptive Forgetting Factor Extreme Learning Machine
孙雪莹; 胡静涛; 王卓; 张吉龙
Source Publication计算机测量与控制
Contribution Rank1
Funding Organization中国科学院重点部署项目(KGZD-EW-302) ; 中国科学院科技服务网络计划(KTJ-SW-STS-159) ; 辽宁省科学技术计划项目(2015020140)
Keyword高炉煤气 在线预测 极限学习机 遗忘因子
Other AbstractBlast furnace gas is an important byproduct in iron and steel plants, and prediction of its generation and consumption has a great effect on balance and scheduling of gas system. However, the accurate prediction poses a significant challenge because of the unstable conditions of the blast furnace gas system and the fluctuation of data. To solve this problem, an online prediction method based on adaptive forgetting factor extreme learning machine ( AF-ELM ) is proposed. Dynamic adaptability of online sequential extreme learning machine is improved by introducing forgetting factor to gradually forget of the old samples. And the forgetting factor is adaptively updated by prediction error, which improves the prediction accuracy. The case study on the online prediction in iron and steel plants shows that compared with on - line sequential extreme learning machine, the proposed method achieve higher prediction accuracy in changing conditions, and more suitable for online prediction of generation and consumption of blast furnace gas
Document Type期刊论文
Corresponding Author孙雪莹
Recommended Citation
GB/T 7714
孙雪莹,胡静涛,王卓,等. 基于自适应遗忘因子极限学习机的高炉煤气预测[J]. 计算机测量与控制,2017,25(7):235-238.
APA 孙雪莹,胡静涛,王卓,&张吉龙.(2017).基于自适应遗忘因子极限学习机的高炉煤气预测.计算机测量与控制,25(7),235-238.
MLA 孙雪莹,et al."基于自适应遗忘因子极限学习机的高炉煤气预测".计算机测量与控制 25.7(2017):235-238.
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