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Multi-kernel learnt partial linear regularization network and its application to predict the liquid steel temperature in ladle furnace.

W. Lv, Z. Mao, P. Yuan, and M. Jia. Knowl.-Based Syst., (2012)

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Corrigendum to "Multi-kernel learnt partial linear regularization network and its application to predict the liquid steel temperature in ladle furnace" Knowl.-Based Syst. 36 (2012) 280-287.W. Lv, Z. Mao, P. Yuan, and M. Jia. Knowl.-Based Syst., (2013)Ensemble fixed-size LS-SVMs applied for the Mach number prediction in transonic wind tunnel.X. Wang, P. Yuan, and Z. Mao. IEEE Trans. Aerospace and Electronic Systems, 51 (4): 3167-3181 (2015)A modified robust self-tuning controller for Hammerstein nonlinear systems with asymmetric deadzone input nonlinearity.B. Zhang, and Z. Mao. J. Systems & Control Engineering, 230 (5): 436-451 (2016)Tree-Structure Ensemble General Regression Neural Networks applied to predict the molten steel temperature in Ladle Furnace.X. Wang, M. You, Z. Mao, and P. Yuan. Advanced Engineering Informatics, 30 (3): 368-375 (2016)Outlier detection based on a dynamic ensemble model: Applied to process monitoring.B. Wang, and Z. Mao. Information Fusion, (2019)Modeling and Optimization of the Drug Extraction Production Process.D. He, Z. Wang, L. Yang, T. Liu, Y. Yao, and Z. Mao. Scientific Programming, (2016)Investigation of nonlinear orthogonal signal correction algorithm and its effects on multivariate calibration.C. Zhao, F. Wang, Z. Mao, and J. Liu. ICARCV, page 2049-2054. IEEE, (2008)Hybrid modeling for the prediction of leaching rate in leaching process based on negative correlation learning bagging ensemble algorithm.G. Hu, Z. Mao, D. He, and F. Yang. Comput. Chem. Eng., 35 (12): 2611-2617 (2011)A general data-driven nonlinear robust optimization framework based on statistic limit and principal component analysis.S. Zhang, R. Jia, D. He, F. Chu, and Z. Mao. Comput. Chem. Eng., (2022)Adaptive control of stochastic Hammerstein-Wiener nonlinear systems with measurement noise.B. Zhang, and Z. Mao. Int. J. Systems Science, 47 (1): 162-178 (2016)

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Multi-kernel learnt partial linear regularization network and its application to predict the liquid steel temperature in ladle furnace.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Multi-kernel learnt partial linear regularization network and its application to predict the liquid steel temperature in ladle furnace.

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Multi-kernel learnt partial linear regularization network and its application to predict the liquid steel temperature in ladle furnace.