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.786 工程科学学报,第42卷,第6期 钢铁研究学报,2019,31(2):174) 密度的计算.钢铁,2004,39(3):29) [3]Li W G,Deng K,Zhao Y T,et al.Self-leamning method for rolling [11]Sun T J,Yang WD,Cheng Y M,et al.Study on multi-objective model based on continuous surface.Iron Steel,2017,52(12):61 optimization strategy of strip steel laminar-cooling system.Control (李维刚,邓肯,赵云涛,等.基于连续曲面的轧制模型自学习方 Eng China,2016,23(1):117 法.钢铁,2017,52(12):61) (孙铁军,杨卫东,程艳明,等.带钢层流冷却系统多目标优化策 [4]Liu X H,Zhao QL,Huang Z Y.Prospect of artificial intelligent 略的研究.控制工程,2016,23(1):117) application in rolling.Steel Roll,2017,34(4):1 [12]Jeong S Y,Lee M,Lee S Y,et al.Improving lookup table control (刘相华,赵启林,黄贞益.人工智能在轧制领域中的应用进展 of a hot coil strip process with online retrained RBF network. 轧钢,2017,34(4):1) IEEE Trans Ind Electron,2000,47(3):679 [5] Edalatpour S,Saboonchi A,Hassanpour S.Effect of phase [13]Fan X M,Zhang L,Cai X H,et al.Hot strip coiling temperature transformation latent heat on prediction accuracy of strip laminar control based on CMAC network.J Northeast Univ Nat Sci,2000, cooling.J Mater Process Tech,2011,211:1776 21(6):662 [6]Hashimoto T.Yoshioka Y,Ohtsuka T.Model predictive control (范晓明,张利,蔡晓辉,等.小脑模型连接控制(CMAC)网络用 for hot strip mill cooling system /2010 IEEE International 于热轧带钢卷取温度控制.东北大学学报:自然科学版,2000, Conference on Control Applications.Yokohama,2010:646 21(6):662) [7]Xu X Q,Hao X D.Fu S L,et al.Cooling route control based on [14]Sun T J,Yang WD,Cheng Y M,et al.Improved genetic algorithm temperature observer for laminar cooling process.J /ron Steel Res, 2017,29(1):81 for optimizing prediction model of strip coiling temperature. (徐小青,郝晓东,傅松林,等.基于温度观测器的层流冷却路径 Control Theory Appl,2015,32(8):1106 (孙铁军,杨卫东,程艳明,等.用改进遗传算法优化的带钢卷取 控制.钢铁研究学报,2017,29(1):81) [8] Song Y,Jing F W.Yin S,et al.High-precision coiling temperature 温度预报模型.控制理论与应用,2015,32(8):1106) control model for heavy gauge strip steel.ChinJ Eng,2015. [15]Pian J X,Zhu Y L.A hybrid soft sensor for measuring hot-rolled 37(1):106 strip temperature in the laminar cooling process. (宋勇,荆丰伟,殷实,等.厚规格热轧带钢高精度卷取温度控制 2015,169:457 模型.工程科学学报,2015,37(1):106) [16]Sato Y,Izui K,Yamada T,et al.Data mining based on clustering [9]Schlang M,Lang B.Poppe T,et al.Current and future and association rule analysis for knowledge discovery in multi- development in neural computation in steel processing.Control objective topology optimization.Expert Syst Appl,2019,119:247 Eng Pract,.2001,9(9):975 [17]He A R,Shao J,Sun W Q.et al.Key precise control technologies [10]Han B,Peng L G,Wang G D,et al.Calculation of basic heat-flux of rolling for smart manufacturing.Metall Ind Autom,2016, density for hot strip laminar cooling system using artificial neural 40(5):1 networks.Iron Steel,2004,39(3):29 (何安瑞,邵健,孙文权,等.适应智能制造的轧制精准控制关键 (韩减,彭良贵,王国栋,等.基于神经网络的热带层流基本热流 技术.冶金自动化,2016,40(5):1)钢铁研究学报, 2019, 31(2):174 ) Li W G, Deng K, Zhao Y T, et al. Self-learning method for rolling model based on continuous surface. Iron Steel, 2017, 52(12): 61 (李维刚, 邓肯, 赵云涛, 等. 基于连续曲面的轧制模型自学习方 法. 钢铁, 2017, 52(12):61 ) [3] Liu X H, Zhao Q L, Huang Z Y. Prospect of artificial intelligent application in rolling. Steel Roll, 2017, 34(4): 1 (刘相华, 赵启林, 黄贞益. 人工智能在轧制领域中的应用进展. 轧钢, 2017, 34(4):1 ) [4] Edalatpour  S,  Saboonchi  A,  Hassanpour  S.  Effect  of  phase transformation latent heat on prediction accuracy of strip laminar cooling. J Mater Process Tech, 2011, 211: 1776 [5] Hashimoto  T,  Yoshioka  Y,  Ohtsuka  T.  Model  predictive  control for  hot  strip  mill  cooling  system  //2010 IEEE International Conference on Control Applications. Yokohama, 2010: 646 [6] Xu X Q, Hao X D, Fu S L, et al. Cooling route control based on temperature observer for laminar cooling process. J Iron Steel Res, 2017, 29(1): 81 (徐小青, 郝晓东, 傅松林, 等. 基于温度观测器的层流冷却路径 控制. 钢铁研究学报, 2017, 29(1):81 ) [7] Song Y, Jing F W, Yin S, et al. High-precision coiling temperature control  model  for  heavy  gauge  strip  steel. Chin J Eng,  2015, 37(1): 106 (宋勇, 荆丰伟, 殷实, 等. 厚规格热轧带钢高精度卷取温度控制 模型. 工程科学学报, 2015, 37(1):106 ) [8] Schlang  M,  Lang  B,  Poppe  T,  et  al.  Current  and  future development  in  neural  computation  in  steel  processing. Control Eng Pract, 2001, 9(9): 975 [9] Han B, Peng L G, Wang G D, et al. Calculation of basic heat-flux density for hot strip laminar cooling system using artificial neural networks. Iron Steel, 2004, 39(3): 29 (韩斌, 彭良贵, 王国栋, 等. 基于神经网络的热带层流基本热流 [10] 密度的计算. 钢铁, 2004, 39(3):29 ) Sun T J, Yang W D, Cheng Y M, et al. Study on multi-objective optimization strategy of strip steel laminar-cooling system. Control Eng China, 2016, 23(1): 117 (孙铁军, 杨卫东, 程艳明, 等. 带钢层流冷却系统多目标优化策 略的研究. 控制工程, 2016, 23(1):117 ) [11] Jeong S Y, Lee M, Lee S Y, et al. Improving lookup table control of  a  hot  coil  strip  process  with  online  retrained  RBF  network. IEEE Trans Ind Electron, 2000, 47(3): 679 [12] Fan X M, Zhang L, Cai X H, et al. Hot strip coiling temperature control based on CMAC network. J Northeast Univ Nat Sci, 2000, 21(6): 662 (范晓明, 张利, 蔡晓辉, 等. 小脑模型连接控制(CMAC)网络用 于热轧带钢卷取温度控制. 东北大学学报: 自然科学版, 2000, 21(6):662 ) [13] Sun T J, Yang W D, Cheng Y M, et al. Improved genetic algorithm for  optimizing  prediction  model  of  strip  coiling  temperature. Control Theory Appl, 2015, 32(8): 1106 (孙铁军, 杨卫东, 程艳明, 等. 用改进遗传算法优化的带钢卷取 温度预报模型. 控制理论与应用, 2015, 32(8):1106 ) [14] Pian J X, Zhu Y L. A hybrid soft sensor for measuring hot-rolled strip temperature in the laminar cooling process. Neurocomputing, 2015, 169: 457 [15] Sato Y, Izui K, Yamada T, et al. Data mining based on clustering and  association  rule  analysis  for  knowledge  discovery  in  multi￾objective topology optimization. Expert Syst Appl, 2019, 119: 247 [16] He A R, Shao J, Sun W Q, et al. Key precise control technologies of  rolling  for  smart  manufacturing. Metall Ind Autom,  2016, 40(5): 1 (何安瑞, 邵健, 孙文权, 等. 适应智能制造的轧制精准控制关键 技术. 冶金自动化, 2016, 40(5):1 ) [17] · 786 · 工程科学学报,第 42 卷,第 6 期
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