Table of Contents Author Guidelines Submit a Manuscript
Journal of Applied Mathematics
Volume 2014, Article ID 298218, 13 pages
http://dx.doi.org/10.1155/2014/298218
Research Article

Quality Prediction and Control of Reducing Pipe Based on EOS-ELM-RPLS Mathematics Modeling Method

1State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110004, China
2Electrical Engineering and Automation Department, Tianjin Polytechnic University, Tianjin 300387, China

Received 27 December 2013; Accepted 12 February 2014; Published 20 March 2014

Academic Editor: Nachamada Blamah

Copyright © 2014 Dong Xiao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. Macrea and C. Cepisca, “Algorithms for speed and stretch control of the main drives of a stretch-reducing tube mill,” Revue Roumaine des Sciences Techniques, Serie Electrotechnique et Energetique, vol. 53, no. 1, pp. 99–107, 2008. View at Google Scholar
  2. L. S. Bayoumi, “Analysis of flow and stresses in a tube stretch-reducing hot rolling schedule,” International Journal of Mechanical Sciences, vol. 45, no. 3, pp. 553–565, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. F.-P. Zhang, B.-Y. Sun, and J.-M. Wang, “Energy method in stretch reducing process of steel tube,” Journal of Iron and Steel Research International, vol. 15, no. 6, pp. 39–43, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. H. Yu, F. Du, and F. Wang, “Finite element model development and application on stretch reducing process of seamless tube,” Journal of Mechanical Engineering, vol. 47, no. 22, pp. 74–79, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. H. Yu, F. S. Du, and F. X. Wang, “FE analysis of tube thickness variation in process of 14-stand slight stretch sizing (reducing) mill,” Steel Pipe, vol. 35, no. 5, pp. 17–20, 2006 (Chinese). View at Google Scholar
  6. W. S. Yi, R. Y. Hao, and H. Yu, “Finite-element analysis and experimental research of in-process gage variation of steel tube being slight-stretch reduced,” Steel Pipe, vol. 30, no. 1, pp. 15–20, 2001 (Chinese). View at Google Scholar
  7. Z. Q. Xu and F. S. Du, “A integrated simulation system of stretch reducing of tube and verification,” Journal of Yanshan University, vol. 28, no. 1, pp. 36–39, 2004 (Chinese). View at Google Scholar
  8. F. S. Du, Q. X. Huang, and C. Liu, “The computer predicting in the process of 3-roll reducing of seamless tube,” Iron and Steel, vol. 30, no. 7, pp. 28–31, 1995 (Chinese). View at Google Scholar
  9. Q. Yuan, L. H. Lv, and P. Liu, “Study of the math model of WT variation in 12-stand mini-stretch reducing mill,” Steel Pipe, vol. 32, no. 6, pp. 5–8, 2003 (Chinese). View at Google Scholar
  10. J. H. Shi, C. J. Zhao, and L. P. Bian, “FEA of oval pass of 21-stand stretch reducing mill,” Steel Pipe, vol. 41, no. 4, pp. 18–22, 2012 (Chinese). View at Google Scholar
  11. Y. Shuang, J. Fan, and M. Lai, “Prediction of accuracy of stretch reduction by artificial neural networks,” Iron and Steel, vol. 35, no. 2, pp. 28–31, 2000 (Chinese). View at Google Scholar · View at Scopus
  12. B. M. Wang, Hot Rolled Steel Tubes Quality, Metallurgical Industry Press of China, Beijing, China, 1987 (Chinese).
  13. S. Wold, N. Kettaneh-Wold, and B. Skagerberg, “Nonlinear PLS modeling,” Chemometrics and Intelligent Laboratory Systems, vol. 7, no. 1-2, pp. 53–65, 1989. View at Publisher · View at Google Scholar · View at Scopus
  14. S. J. Qin, “Recursive PLS algorithms for adaptive data modeling,” Computers & Chemical Engineering, vol. 22, no. 4-5, pp. 503–514, 1998. View at Publisher · View at Google Scholar · View at Scopus
  15. B. Hu, Z. Zhao, and J. Liang, “Multi-loop nonlinear internal model controller design under nonlinear dynamic PLS framework using ARX-neural network model,” Journal of Process Control, vol. 22, no. 1, pp. 207–217, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. G. Feng, Z. Qian, and N. Dai, “Reversible watermarking via extreme learning machine prediction,” Neurocomputing, vol. 82, pp. 62–68, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. Y. Yu, T.-M. Choi, and C.-L. Hui, “An intelligent quick prediction algorithm with applications in industrial control and loading problems,” IEEE Transactions on Automation Science and Engineering, vol. 9, no. 2, pp. 276–287, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. Y. M. Yang, Y. N. Wang, and X. F. Yuan, “Bidirectional extreme learning machine for regression problem and its learning effectiveness,” IEEE Transaction on Neural Network, vol. 23, no. 9, pp. 1498–1505, 2012. View at Publisher · View at Google Scholar
  19. G.-B. Huang, H. Zhou, X. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification,” IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics, vol. 42, no. 2, pp. 513–529, 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. H.-X. Tian and Z.-Z. Mao, “An ensemble ELM based on modified AdaBoost.RT algorithm for predicting the temperature of molten steel in ladle furnace,” IEEE Transactions on Automation Science and Engineering, vol. 7, no. 1, pp. 73–80, 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. S. J. Xie, J. Yang, H. Gong, S. Yoon, and D. S. Park, “Intelligent fingerprint quality analysis using online sequential extreme learning machine,” Soft Computing, vol. 16, no. 9, pp. 1555–1568, 2012. View at Publisher · View at Google Scholar · View at Scopus
  22. J. Zhao, Z. Wang, and D. S. Park, “Online sequential extreme learning machine with forgetting mechanism,” Neurocomputing, vol. 87, pp. 79–89, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. Y. Lan, Y. C. Soh, and G.-B. Huang, “Ensemble of online sequential extreme learning machine,” Neurocomputing, vol. 72, no. 13–15, pp. 3391–3395, 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. J. H. Lee, K. S. Lee, and W. C. Kim, “Model-based iterative learning control with a quadratic criterion for time-varying linear systems,” Automatica, vol. 36, no. 5, pp. 641–657, 2000. View at Publisher · View at Google Scholar · View at MathSciNet