Q. Feng, T. Li, X. Fan, and T. Jiang, Adaptive prediction system of sintering through point based on self-organize artificial neural network, Trans. Nonferrous Met. Soc. China, vol.10, issue.6, pp.804-807, 2000.

L. Peng, Z. Ji, and J. Tan, Sintering finish point intelligent control, Proceedings of the, 2005.

, IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp.24-28, 2005.

S. Du, M. Wu, X. Chen, X. Lai, and W. Cao, Intelligent coordinating control between burn-through point and mixture bunker level in an iron ore sintering process, Journal of advanced Computational Intelligence and Intelligent Informatics, vol.21, issue.1, pp.140-147, 2017.

M. Wu, C. Xu, and Y. Du, Intelligent optimal control for lead-zinc sintering process state, Trans. Nonferrous Met. Soc, China, vol.16, pp.975-981, 2006.

M. Wu, C. Xu, J. She, and W. Cao, Neural-network-based integrated model for predicting burnthrough point in lead-zinc sintering process, Journal of Process Control, vol.22, pp.925-934, 2012.

M. Wu, P. Duan, W. Cao, J. She, and J. Xiang, An intelligent control system based on prediction of the burn-through point for the sintering process of an iron and steel plant, Expert Systems with Applications, vol.39, issue.5, pp.5971-5981, 2012.

W. Cheng, An application of adaptive genetic-neural algorithm to sinter's BTP process, Proceedings of the Third Intemational Conference on Machine Learning and Cybernetics, pp.3356-3360, 2004.

W. Cheng, Prediction system of burning through point (BTP) based on adaptive pattern clustering and feature map, Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, pp.3089-3094, 2006.

X. Shang, J. Lu, Y. Sun, J. Liu, and Y. Ying, Data-driven prediction of sintering burn-through point based on novel genetic programming, Journal of iron and steel research, International, vol.17, issue.12, pp.1-10, 2010.

D. Wang, K. Yang, Z. He, Y. Yuan, and J. Zhang, Application Research Based on GA-FWA in Prediction of Sintering Burning Through Point, Proceedings of 2018 International Conference on Computer, pp.378-385, 2018.

W. H. Kwon, Y. H. Kim, S. J. Lee, and K. Paek, Event-based modeling and control for the burnthrough point in sintering processes, IEEE Transactions On Control Systems Technology, vol.7, issue.1, pp.31-41, 1999.

J. Terpak, L. Dorcak, I. Kostial, and L. Pivka, Control of burn-through point for agglomeration belt, Metalurgia, vol.44, issue.4, pp.281-284, 2005.

C. Wang and M. Wu, Hierarchical intelligent control system and its application to the sintering process, IEEE Transactions On Industrial Informatics, vol.2013, issue.1, pp.190-196

M. Wu, C. Wang, W. Cao, X. Lai, and X. Chen, Design and application of generalized predictive control strategy with closed-loop identification for burn-through point in sintering process, Control Engineering Practice, vol.20, pp.1065-1074, 2012.

J. Shi, Y. Wu, L. Liao, X. Yan, J. Zeng et al., Soft sensing of the burning through point in iron-making process, Proceedings of 2016 IEEE 15th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC, 2016.

J. Deng, Introduction to grey system theory. The Journal of Grey System, vol.1, pp.1-24, 1989.

T. L. Tien, The indirect measurement of tensile strength of material by the grey prediction model GMC(1,n), Measurement Science Technology, vol.16, pp.1322-1328, 2005.

Y. Kaymak, T. Hauck, and M. Hillers, Iron ore sintering process model to study local permeability control, Proceedings of the 2017 COMSOL Conference in Rotterdam, 2017.

T. Tien, A research on the grey prediction model GM(1,n), Applied Mathematics and Computation, vol.218, pp.4903-4916, 2012.

T. Tien, The indirect measurement of tensile strength for a higher temperature by the new model IGDMC(1,n). Measurement, vol.41, pp.662-675, 2008.

T. Tien, The indirect measurement of tensile strength by the new model FGMC (1,n). Measurement, vol.44, pp.1884-1897, 2011.

T. Tien, The deterministic grey dynamic model with convolution integral DGDMC(1,n), Applied Mathematical Modelling, vol.33, pp.3498-3510, 2009.

J. Kennedy and R. Eberhart, Particle Swarm Optimization, 1995.

A. M. Abdulshahed, A. P. Longstaff, and S. Fletcher, A cuckoo search optimization-based Grey prediction model for thermal error compensation on CNC machine tools. Grey Systems: Theory and Application, vol.7, pp.146-1551, 2017.

L. Wu and Z. Zhang, Grey multivariable convolution model with new information priority accumulation, Applied Mathematical Modelling, vol.62, pp.595-604, 2018.

S. A. Javed and S. Liu, Bidirectional Absolute GRA/GIA model for Uncertain Systems: Application in Project Management, IEEE Access, vol.2019, pp.1-9