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576 工程科学学报,第43卷,第4期 ISUm,2019,59(9):1534 39 [14]Su X L,Zhang S,Yin Y X,et al.Prediction model of hot metal (张海刚,张森,尹怡欣.基于全局优化支持向量机的多类别高 temperature for blast furnace based on improved multi-layer 炉故障诊断.工程科学学报,2017,39(1):39) extreme leaming machine.Int J Mach Learn Cybern,2019, [21]Chu F,Ye J F,Ma X P,et al.Online performance prediction of 10(10):2739 CCPP byproduct coal-gas system based on online sequential [15]Zhao H,Zhao D T,Yue Y J,et al.Study on prediction method of extreme learning machine.ChinJ Eng,2016,38(6):861 hot metal temperature in blast fumace IEEE International (褚菲,叶俊锋,马小平,等.基于OS-ELM的CCPP副产煤气燃料 Conference on Mechatronics and Automation.Takamatsu,2017: 系统在线性能预测.工程科学学报,2016,38(6):861) 316 [22]Li A L,Zhao Y M,Cui G M.Prediction model of blast furnace [16]Wang Y K,Liu X Y,Zhang B L.On feature selection and blast temperature based on ELM with grey correlation analysis.J Iron fumace temperature tendency prediction in hot metal based on Steel Res,.2015,27(11):33 SVM-RFE II Australian and New Zealand Control Conference (李爱莲,赵永明,崔桂梅.基于灰色关联分析的ELM高炉温度 Melbourne,2018:371 预测模型.钢铁研究学报,2015,27(11):33) [17]Yue Y J.Dong A.Zhao H,et al.Study on prediction model of [23]Chen H Z,Yang J P,Lu X C,et al.Quality prediction of the blast fumace hot metal temperature /IEEE International continuous casting bloom based on the extreme learning machine. Conference on Mechatronics and Automation.Harbin,2016:1396 Chin J Eng,2018,40(7):815 [18]Liu Y,Gao Z.Enhanced just-in-time modelling for online quality (陈恒志,杨建平,卢新春,等.基于极限学习机(ELM)的连铸坯 prediction in BF ironmaking.Ironmaking Steelmaking,2015, 质量预测.工程科学学报,2018,40(7):815) 42(5):321 [24]Chen P,Li Q,Zhang D Z,et al.A survey of multimodal machine [19]Guo D W,Zhou P.Soft-sensor modeling of silicon content in hot learning.Chin J Eng,2020,42(5):557 metal based on sparse robust LS-SVR and multi-objective (陈鹏,李擎,张德政,等.多模态学习方法综述.工程科学学报, optimization.Chin J Eng,2016,38(9):1233 2020,42(5):557) (郭东伟,周平,基于稀疏化鲁棒LS-SVR与多目标优化的铁水硅 [25]Tunckaya Y.Performance assessment of permeability index 含量软测量建模.工程科学学报,2016,38(9):1233) prediction in an ironmaking process via soft computing techniques. [20]Zhang H G,Zhang S,Yin Y X.Multi-class fault diagnosis of BF Proc Inst Mech Eng Part E-J Process Mech Eng,2017,231(6): based on global optimization LS-SVM.Chin J Eng,2017,39(1) 1101ISIJ Int, 2019, 59(9): 1534 Su X L, Zhang S, Yin Y X, et al. Prediction model of hot metal temperature  for  blast  furnace  based  on  improved  multi-layer extreme  learning  machine. Int J Mach Learn Cybern,  2019, 10(10): 2739 [14] Zhao H, Zhao D T, Yue Y J, et al. Study on prediction method of hot  metal  temperature  in  blast  furnace  // IEEE International Conference on Mechatronics and Automation. Takamatsu,  2017: 316 [15] Wang Y K, Liu X Y, Zhang B L. On feature selection and blast furnace  temperature  tendency  prediction  in  hot  metal  based  on SVM-RFE  // Australian and New Zealand Control Conference. Melbourne, 2018: 371 [16] Yue  Y  J,  Dong  A,  Zhao  H,  et  al.  Study  on  prediction  model  of blast  furnace  hot  metal  temperature  // IEEE International Conference on Mechatronics and Automation. Harbin, 2016: 1396 [17] Liu Y, Gao Z. Enhanced just-in-time modelling for online quality prediction  in  BF  ironmaking. Ironmaking Steelmaking,  2015, 42(5): 321 [18] Guo D W, Zhou P. Soft-sensor modeling of silicon content in hot metal  based  on  sparse  robust  LS-SVR  and  multi-objective optimization. Chin J Eng, 2016, 38(9): 1233 (郭东伟, 周平. 基于稀疏化鲁棒LS-SVR与多目标优化的铁水硅 含量软测量建模. 工程科学学报, 2016, 38(9):1233) [19] Zhang H G, Zhang S, Yin Y X. Multi-class fault diagnosis of BF based on global optimization LS-SVM. Chin J Eng, 2017, 39(1): [20] 39 (张海刚, 张森, 尹怡欣. 基于全局优化支持向量机的多类别高 炉故障诊断. 工程科学学报, 2017, 39(1):39) Chu  F,  Ye  J  F,  Ma  X  P,  et  al.  Online  performance  prediction  of CCPP  byproduct  coal-gas  system  based  on  online  sequential extreme learning machine. Chin J Eng, 2016, 38(6): 861 (褚菲, 叶俊锋, 马小平, 等. 基于OS-ELM的CCPP副产煤气燃料 系统在线性能预测. 工程科学学报, 2016, 38(6):861) [21] Li  A  L,  Zhao  Y  M,  Cui  G  M.  Prediction  model  of  blast  furnace temperature based on ELM with grey correlation analysis. J Iron Steel Res, 2015, 27(11): 33 (李爱莲, 赵永明, 崔桂梅. 基于灰色关联分析的ELM高炉温度 预测模型. 钢铁研究学报, 2015, 27(11):33) [22] Chen  H  Z,  Yang  J  P,  Lu  X  C,  et  al.  Quality  prediction  of  the continuous casting bloom based on the extreme learning machine. Chin J Eng, 2018, 40(7): 815 (陈恒志, 杨建平, 卢新春, 等. 基于极限学习机(ELM)的连铸坯 质量预测. 工程科学学报, 2018, 40(7):815) [23] Chen P, Li Q, Zhang D Z, et al. A survey of multimodal machine learning. Chin J Eng, 2020, 42(5): 557 (陈鹏, 李擎, 张德政, 等. 多模态学习方法综述. 工程科学学报, 2020, 42(5):557) [24] Tunckaya  Y.  Performance  assessment  of  permeability  index prediction in an ironmaking process via soft computing techniques. Proc Inst Mech Eng Part E-J Process Mech Eng,  2017,  231(6): 1101 [25] · 576 · 工程科学学报,第 43 卷,第 4 期
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