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Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism
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作者 Runhao Zhang Jian Yang +1 位作者 Han Sun Wenkui Yang 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2024年第3期508-517,共10页
The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting me... The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction. 展开更多
关键词 basic oxygen furnace steelmaking machine learning lime utilization ratio DEPHOSPHORIZATION online sequential extreme learning machine forgetting mechanism
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基于CPA-OSELM的热轧带钢厚度在线预测
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作者 肖思竹 张飞 +2 位作者 黄学忠 肖雄 易忠荣 《科学技术与工程》 北大核心 2022年第22期9686-9694,共9页
为解决自动厚度控制(automatic gauge control, AGC)系统反馈滞后、耦合强、厚度偏差大等问题,提出了一种基于食肉植物算法(carnivorous plant algorithm, CPA)的在线顺序极限学习机(online sequential extreme learning machine, OSELM... 为解决自动厚度控制(automatic gauge control, AGC)系统反馈滞后、耦合强、厚度偏差大等问题,提出了一种基于食肉植物算法(carnivorous plant algorithm, CPA)的在线顺序极限学习机(online sequential extreme learning machine, OSELM)预测算法。首先,基于从现场采集的相关数据,建立了OSELM在线厚度预测模型。然后为了提高模型的准确性及稳定性,采用CPA方法优化OSELM的权重和偏置。在此基础上,运用自学习方法进一步提高模型的预测精度。最后,通过实验验证基于CPA-OSELM预测模型的有效性。实验结果表明:基于CPA-OSELM的方法能够对不同规格带钢的出口厚度进行高精度在线预测,预测结果可用于提升AGC模型的控制精度,为提升带钢产品质量奠定基础。 展开更多
关键词 热轧带钢 在线预测 在线顺序极限学习机(online sequential extreme learning machine OSELM) 食肉植物算法(carnivorous plant algorithm CPA) 自学习
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Efficient Model Store and Reuse in an OLML Database System
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作者 Jian-Wei Cui Wei Lu +1 位作者 Xin Zhao Xiao-Yong Du 《Journal of Computer Science & Technology》 SCIE EI CSCD 2021年第4期792-805,共14页
Deep learning has shown significant improvements on various machine learning tasks by introducing a wide spectrum of neural network models.Yet,for these neural network models,it is necessary to label a tremendous amou... Deep learning has shown significant improvements on various machine learning tasks by introducing a wide spectrum of neural network models.Yet,for these neural network models,it is necessary to label a tremendous amount of training data,which is prohibitively expensive in reality.In this paper,we propose OnLine Machine Learning(OLML)database which stores trained models and reuses these models in a new training task to achieve a better training effect with a small amount of training data.An efficient model reuse algorithm AdaReuse is developed in the OLML database.Specifically,AdaReuse firstly estimates the reuse potential of trained models from domain relatedness and model quality,through which a group of trained models with high reuse potential for the training task could be selected efficiently.Then,multi selected models will be trained iteratively to encourage diverse models,with which a better training effect could be achieved by ensemble.We evaluate AdaReuse on two types of natural language processing(NLP)tasks,and the results show AdaReuse could improve the training effect significantly compared with models training from scratch when the training data is limited.Based on AdaReuse,we implement an OLML database prototype system which could accept a training task as an SQL-like query and automatically generate a training plan by selecting and reusing trained models.Usability studies are conducted to illustrate the OLML database could properly store the trained models,and reuse the trained models efficiently in new training tasks. 展开更多
关键词 model selection model reuse online machine learning(OLML)database
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Adaptive forgetting factor OS-ELM and bootstrap for time series prediction 被引量:1
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作者 Jingzhong Liu 《International Journal of Modeling, Simulation, and Scientific Computing》 EI 2017年第3期159-177,共19页
Online sequential extreme learning machine(OS-ELM)for single-hidden layer feedforward networks(SLFNs)is an effective machine learning algorithm.But OS-ELM has some underlying weaknesses of neglecting time series timel... Online sequential extreme learning machine(OS-ELM)for single-hidden layer feedforward networks(SLFNs)is an effective machine learning algorithm.But OS-ELM has some underlying weaknesses of neglecting time series timeliness and being incapable to provide quantitative uncertainty for prediction.To overcome these shortcomings,a time series prediction method is presented based on the combination of OS-ELM with adaptive forgetting factor(AFF-OS-ELM)and bootstrap(B-AFF-OS-ELM).Firstly,adaptive forgetting factor is added into OS-ELM for adjusting the effective window length of training data during OS-ELM sequential learning phase.Secondly,the current bootstrap is developed to fit time series prediction online.Then associated with improved bootstrap,the proposed method can compute prediction interval as uncertainty information,meanwhile the improved bootstrap enhances prediction accuracy and stability of AFF-OS-ELM.Performances of B-AFF-OS-ELM are benchmarked with other traditional and improved OS-ELM on simulation and practical time series data.Results indicate the significant performances achieved by B-AFF-OS-ELM. 展开更多
关键词 online sequential extreme learning machine(OS-ELM) l2-regularization forecasting uncertainty prediction interval ENSEMBLE chaotic time series neural networks
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