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Mathematical Problems in Engineering
Volume 2014, Article ID 681259, 13 pages
http://dx.doi.org/10.1155/2014/681259
Research Article

D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process

1College of Information and Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China
2Multi-Functional Design and Research Academy, Zhengzhou University, Zhengzhou 450001, China
3School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114044, China

Received 23 December 2013; Accepted 18 March 2014; Published 15 April 2014

Academic Editor: Qintao Gan

Copyright © 2014 Shu-zhi Gao 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. J. S. Wang, S. Han, and Q. P. Guo, “Echo state networks based predictive model of vinyl chloride monomer convention velocity optimized by artificial fish swarm algorithm,” Soft Computing, vol. 18, no. 3, pp. 457–468, 2014. View at Publisher · View at Google Scholar
  2. Z. G. Hui, “The application of stripping technology in the production of PVC,” Polyvinyl Chloride, vol. 7, no. 2, pp. 8–12, 2007. View at Google Scholar
  3. H. Li, “Design of Multivariable Fuzzy-neural Network Decoupling Controller,” Control and Decision, vol. 21, no. 5, pp. 593–596, 2006. View at Google Scholar
  4. W. G. Xiao, Z. M. Wang, Y. F. Guo et al., “PVC boiling bed drying process optimization control system,” Automation and Instruments in Chemical Industry, vol. 21, no. 5, pp. 12–15, 1994. View at Google Scholar
  5. M. Y. Khairiyah, C. E. Boo, and T. S. Amy, Formulation of Model Predictive Control Algorithm For Nonlinear Processes, University of Technology Malaysia, 2006.
  6. S. Q. Wu and J. Xu, Dynamic Fuzzy Neural Network-Design and Application, Tsinghua University Press, Beijing, China, 2008.
  7. J. S. Wang and Q. P. Guo, “D-FNN based soft-sensor modeling and migration reconfiguration of polymerizing process,” Applied Soft Computing, vol. 13, no. 4, pp. 1892–1901, 2013. View at Google Scholar
  8. C. K. Kwong, T. C. Wong, and K. Y. Chan, “A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach,” Expert Systems with Applications, vol. 36, no. 8, pp. 11262–11270, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. K. Y. Chan, C. K. Kwong, and Y. C. Tsim, “Modelling and optimization of fluid dispensing for electronic packaging using neural fuzzy networks and genetic algorithms,” Engineering Applications of Artificial Intelligence, vol. 23, no. 1, pp. 18–26, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. C. K. Kwong, K. Y. Chan, and Y. C. Tsim, “A genetic algorithm based knowledge discovery system for the design of fluid dispensing processes for electronic packaging,” Expert Systems with Applications, vol. 36, no. 2, pp. 3829–3838, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. R. L. Liu, H. Y. Su, and J. Chu, “A soft sensor modeling algorithm based on modified fuzzy neural network,” Information and Control, vol. 32, no. 4, pp. 367–370, 2003. View at Google Scholar
  12. G.-C. Chen, Y.-F. Xu, and J.-S. Yu, “Soft-sensor modelling of acrylonitrile yield based on particle swarm optimization fuzzy neural networks,” Journal of System Simulation, vol. 19, no. 23, pp. 5370–5372, 2007. View at Google Scholar · View at Scopus
  13. I. Li and L. W. Lee, “Interval type 2 hierarchical FNN with the H-infinity condition for MIMO non-affine systems,” Applied Soft Computing, vol. 12, no. 8, pp. 1996–2011, 2012. View at Publisher · View at Google Scholar
  14. H.-K. Wu, J.-H. Hsieh, Y.-L. Lin, and J.-H. Jeng, “On maximum likelihood fuzzy neural networks,” Fuzzy Sets and Systems, vol. 161, no. 21, pp. 2795–2807, 2010. View at Publisher · View at Google Scholar
  15. M. B. Nasr and M. Chtourou, “A self-organizing map-based initialization for hybrid training of feedforward neural networks,” Applied Soft Computing Journal, vol. 11, no. 8, pp. 4458–4464, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. H. C. Lu, M. H. Chang, and C. H. Tsai, “Adaptive self-constructing fuzzy neural network controller for hardware implementation of an inverted pendulum system,” Applied Soft Computing, vol. 11, no. 5, pp. 3962–3975, 2011. View at Google Scholar
  17. S. Wu and M. J. Er, “Dynamic fuzzy neural networks—a novel approach to function approximation,” IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics, vol. 30, no. 2, pp. 358–364, 2000. View at Publisher · View at Google Scholar · View at Scopus
  18. F. J. Lin, C. H. Lin, and P. H. Shen, “Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive. Fuzzy Systems,” IEEE Transactions on Fuzzy Systems, vol. 9, no. 5, pp. 751–759, 2001. View at Google Scholar
  19. X. Deng and X. Wang, “Incremental learning of dynamic fuzzy neural networks for accurate system modeling,” Fuzzy Sets and Systems, vol. 160, no. 7, pp. 972–987, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  20. C.-F. Juang and C.-D. Hsieh, “A locally recurrent fuzzy neural network with support vector regression for dynamic-system modeling,” IEEE Transactions on Fuzzy Systems, vol. 18, no. 2, pp. 261–273, 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. A. Subasi, “Automatic detection of epileptic seizure using dynamic fuzzy neural networks,” Expert Systems with Applications, vol. 31, no. 2, pp. 320–328, 2006. View at Publisher · View at Google Scholar · View at Scopus
  22. H. Adeli and X. Jiang, “Dynamic fuzzy wavelet neural network model for structural system identification,” Journal of Structural Engineering, vol. 132, no. 1, pp. 102–111, 2006. View at Publisher · View at Google Scholar · View at Scopus
  23. C.-H. Lee and C.-C. Teng, “Identification and control of dynamic systems using recurrent fuzzy neural networks,” IEEE Transactions on Fuzzy Systems, vol. 8, no. 4, pp. 349–366, 2000. View at Publisher · View at Google Scholar · View at Scopus
  24. B. H. M. Sadeghi, “BP-neural network predictor model for plastic injection molding process,” Journal of Materials Processing Technology, vol. 103, no. 3, pp. 411–416, 2000. View at Publisher · View at Google Scholar · View at Scopus
  25. Z. Xiao, S.-J. Ye, B. Zhong, and C.-X. Sun, “BP neural network with rough set for short term load forecasting,” Expert Systems with Applications, vol. 36, no. 1, pp. 273–279, 2009. View at Publisher · View at Google Scholar · View at Scopus
  26. J. Yi, Q. Wang, D. Zhao, and J. T. Wen, “BP neural network prediction-based variable-period sampling approach for networked control systems,” Applied Mathematics and Computation, vol. 185, no. 2, pp. 976–988, 2007. View at Publisher · View at Google Scholar · View at Scopus