Advertisement

Research on hot-rolling steel products quality control based on BP neural network inverse model

  • Shiyi Xing
  • Jianguo Ju
  • Jinsheng Xing
S.I. : Emerging Intelligent Algorithms for Edge-of-Things Computing
  • 25 Downloads

Abstract

Taking the hot-rolling products made by an iron and steel company as the research object, this paper builds the inverse model reflecting the relationship between the hot-rolling steel product performance indicators, the chemical composition of steel and the rolling technological parameters by using the BP neural network. So the purpose of getting technological parameters is achieved, according to the given steel performance indicators. Combining the BP neural network, adaptive inverse control with internal model control theory, this paper builds the BP neural network inverse model with multiple input and single output based on internal model control. Therefore, it realizes the inverse mapping between the output and the input variables of the BP neural network. And the output variables can be obtained according to the input variables. Besides, this paper also gives the detailed steps to solve the inverse model. Then, the model is applied to the hot-rolling steel products quality control system. The performance indicators of the hot-rolling products are set up, and the rolling technological parameters—the rolling crimp temperature—are solved. The model realizes the controllability of rolling technological parameters. Finally, through the verification of hot-rolling products quality control positive system, the error is in line with the enterprise production requirements.

Keywords

BP neural network Adaptive inverse control Internal model control Hot-rolling products Quality control 

Notes

Acknowledgements

This project was supported by Shanxi Soft Science Fund Project of China (2011041033-03). The authors would like to express thanks for the anonymous reviewers’ and the editors’ instructive suggestions which to a great degree improved the quality of this paper.

Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

References

  1. 1.
    Cochofel HJ, Wooten D, Principe J (1998) A neural network development environment for adaptive inverse control. In: IEEE international joint conference on neural networks proceedings, 1998. IEEE world congress on computational intelligence, vol 2. IEEE, pp 963–967Google Scholar
  2. 2.
    Ruslan FA, Samad AM, Zain ZM et al (2014) Modelling flood prediction using Radial Basis Function Neural Network (RBFNN) and inverse model: a comparative study. In: IEEE international conference on control system, computing and engineering. IEEE, pp 577–581Google Scholar
  3. 3.
    Kabir H, Wang Y, Yu M et al (2008) Neural network inverse modeling and applications to microwave filter design. IEEE Trans Microw Theory Tech 56(4):867–879CrossRefGoogle Scholar
  4. 4.
    Karshenas M, Dunnigan MW, Williams BW (2000) Adaptive inverse control algorithm for shock testing. IEE Proc Control Theory Appl 147(3):267–276CrossRefGoogle Scholar
  5. 5.
    Plett GL (2003) Adaptive inverse control of linear and nonlinear systems using dynamic neural networks. IEEE Trans Neural Netw 14(2):360CrossRefGoogle Scholar
  6. 6.
    Lobato J, Cañizares P, Rodrigo MA et al (2010) Direct and inverse neural networks modelling applied to study the influence of the gas diffusion layer properties on PBI-based PEM fuel cells. Int J Hydrogen Energy 35(15):7889–7897CrossRefGoogle Scholar
  7. 7.
    Hattab N, Motelica-Heino M (2014) Application of an inverse neural network model for the identification of optimal amendment to reduce copper toxicity in phytoremediated contaminated soils. J Geochem Explor 136(1):14–23CrossRefGoogle Scholar
  8. 8.
    Li J, Li S, Chen X et al (2014) RBFNDOB-based neural network inverse control for non-minimum phase MIMO system with disturbances. ISA Trans 53(4):983CrossRefGoogle Scholar
  9. 9.
    Sun L, Li Y, Li D (2014) Delay separated neural network inverse control in main-steam temperature system. Telkomnika Indones J Electr Eng 12(7):5244–5250Google Scholar
  10. 10.
    Harnold C, Lee KY (2002) Application of the free-model based neural networks in model reference adaptive inverse control. In: Proceedings of the American control conference, 2000, vol 3. IEEE, pp 1664–1668Google Scholar
  11. 11.
    Ding S, Wu Q (2011) Research on robustness of BP neural network based inverse model for induction motor drives. In: International conference on electronics and optoelectronics. IEEE, pp V2-127–V2-131Google Scholar
  12. 12.
    Várkonyi-Kóczy AR (2009) Observer-based iterative fuzzy and neural network model inversion for measurement and control applications. In: Rudas IJ, Fodor J, Kacprzyk J et al (eds) Towards intelligent engineering and information technology, SCI, vol 243. Springer, Heidelberg, pp 681–702CrossRefGoogle Scholar
  13. 13.
    Wang P, Cheng B, Xing W et al (2010) The direct inverse-model control based on neural networks for inverts. In: International conference on measuring technology and mechatronics automation. IEEE, pp 855–858Google Scholar
  14. 14.
    Widrow B, Bilello M (1997) Adaptive inverse control. In: IEEE international symposium on intelligent control. IEEE, pp 1–6Google Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.College of Information EngineeringNorthwest A&F UniversityXi’anChina
  2. 2.School of Mathematics and Computer ScienceShanxi Normal UniversityLinfen CityChina

Personalised recommendations