Application of the improved the ELM algorithm for prediction of blast furnace gas utilization rate
Tài liệu tham khảo
Yu, 2011, Blast furnace energy efficiency modeling and analysis [J], Manufacturing Automation, 142
Jianqi, 2015, Prediction method of carbon monoxide utilization in blast furnace based on improved support vector machine [J], Journal of Chemical Industry and Engineering, 06
Lin, 2016, Recognition of Blast Furnace Gas Flow Center Distribution Based on Infrared Image Processing[J], Journal of Iron and Steel Research, 23, 203, 10.1016/S1006-706X(16)30035-8
Shaogang, 2017, Current status and analysis of utilization rate of blast furnace gas in Panzhihua Iron and Steel Corporation [J], Sichuan Metallurgy, 13
Nan, 2014
Huang, 2004, Extreme learning machine: a new learning scheme of feedforward neural networks[J], Proc.int.jointConf.neuralNetw, 22004, 22004
Huang, 2006, Extreme learning machine: Theory and applications [J], Neurocomputing, 70, 10.1016/j.neucom.2005.12.126
Liang, 2006, A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks [J], IEEE Transactions on Neural Networks, 17
Ailian, 2015, ELM blast furnace temperature prediction model based on grey correlation analysis[J], Journal of Iron and Steel Research, 27
Zhang, 2014, A Novel Improved ELM Algorithm for a Real Industrial Application[J], Mathematical Problems in Engineering
Uemukai, 2012, Erratum to: Small sample properties of a ridge regression estimator when there exist omitted variables[J], Statistical Papers, 53, 10.1007/s00362-011-0413-2
Zhang, 2015, An Improved ELM Algorithm Based on PCA Technique[M], Springer International Publishing, 95