| ［1］Orowan E. The Calculation of Roll Pressure in Hot and Cold Flat Rolling［J］. Archive Proceedings of the Institution of Mechanical Engineers, 1943, 150:140-167.
［2］Kim D J, Kim Y C, Kim B M. Optimization of the irregular shape rolling process with an artificial neural network［J］. Journal of Materials Processing Technology, 2001, 113(1-3):131-135.
［3］Reddy N V, Suryanarayana G. A set-up model for tandem cold rolling mills［J］. Journal of Materials Processing Technology, 2001, 116(2): 269-277.
［4］赵志伟, 杨景明, 车海军, 等. 基于人工蜂群算法与反向传播神经网络的铝热连轧轧制力预测［J］. 计量学报, 2014, 35(2): 157-160.
Zhao Z W, Yang J M, Che H J, et al. Prediction of Rolling Force Based on Artificial Bee Colony Algorithm and Back Propagation Neural Network in Aluminum Hot Tandem Rolling［J］. Acta Metrologica Sinica, 2014, 35(2): 157-160.
［5］Zhao Z W, Yang J M, Che H, et al. Application of artificial bee colony algorithm to select architecture of a optimal neural network for the prediction of rolling force in hot strip rolling process［J］. Journal of Chemical and Pharmaceutical Research, 2013, 5(9): 563-570.
［6］Huang G B, Chen L. Enhanced random search based incremental extreme learning machine［J］.Neurocomputing, 2008, 71(16-18): 3460-3468.
［7］Jun Y, Er M J. An Enhanced Online Sequential Extreme Learning Machine Algorithm［C］//IEEE.2008 Chinese Control And Decision Conference. Yantai,China, 2008: 2 902-2 907.
［8］王荣军, 马立峰, 蒋亚平, 等. AZ31B镁合金变形抗力数学模型的建立［J］. 材料热处理学报, 2017, 38(2): 168-172.
Wang R J, Ma L F, Jiang Y P, et al. Mathematical model of deformation resistance for AZ31B magnesium alloy［J］. Transactions of Materials and Heat Treatment, 2017, 38(2): 168-172.
［9］Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: Theory and applications［J］. Neurocomputing, 2006, 70(1-3): 489-501.
［10］何群, 王红, 江国乾, 等. 基于相关主成分分析和极限学习机的风电机组主轴承状态监测研究［J］. 计量学报, 2018, 39(1): 89-93.
He Q, Wang H, Jiang G Q, et al. Research of Wind Turbine Main Bearing Condition Monitoring Based on Correlation PCA and ELM［J］. Acta Metrologica Sinica, 2018, 39(1): 89-93.
［11］杨景明, 马凤艳, 车海军, 等. 基于最小二乘支持向量机的反向建模的铝合金变形抗力模型［J］. 计量学报, 2013, 34(6): 532-536.
Yang J M, Ma F Y, Che H J, et al. Deformation Resistance Model of Aluminum Alloy Based on Least Squares Support Vector Machine Reversed Modeling Method［J］. Acta Metrologica Sinica, 2013, 34(6): 532-536.
［12］孙浩, 杨景明, 呼子宇, 等. 基于改进鱼群算法的铝热连轧摩擦系数模型研究［J］. 计量学报, 2016, 37(1): 53-55.
Sun H, Yang J M, Hu Z Y, et al. Study on Aluminum Hot Rolling Model of Friction Coefficient Based on Improved Fish Swarm Algorithm［J］. Acta Metrologica Sinica, 2016, 37(1): 53-55.
［13］Sun J, Wu X, Palade V, et al. Convergence analysis and improvements of quantum-behaved particle swarm optimization［J］. Information Sciences, 2012, 193: 81-103.
［14］Shao Z, Er M J. An online sequential learning algorithm for regularized Extreme Learning Machine［J］. Neurocomputing, 2016, 173: 778-788.
［15］Nwachukwu P U, Oluwole O O. Effects of rolling process parameters on the mechanical properties of hot-rolled St60Mn steel［J］. Case Studies in Construction Materials, 2017, 6: 134-146.
［16］李荣雨, 戚桂洪. 基于CSSE-OSELM算法的软测量建模及其工业应用［J］. 计量学报, 2017, 38(5): 650-655.
Li R Y,Qi G H. Soft Sensor Modeling Based on CSSE-OSELM Algorithm and Its Industrial Application［J］. Acta Metrologica Sinica, 2017, 38(5): 650-655.
［17］韩红桂, 乔俊飞, 薄迎春. 基于信息强度的RBF神经网络结构设计研究［J］. 自动化学报, 2012, 38(7): 1083-1090.
Han H G, Qiao J F, Bo Y C. On Structure Design for RBF Neural Network Based on Information Strength［J］, Acta Automatica Sinica, 2012, 38(7): 1083-1090. |
2019, Vol. 40 
