Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 582571, 6 pages
http://dx.doi.org/10.1155/2014/582571
Research Article

Intelligent Integration between Human Simulated Intelligence and Expert Control Technology for the Combustion Process of Gas Heating Furnace

1College of Electrical & Information Engineering, Chongqing University of Science & Technology, Shapingba, Chongqing 401331, China
2School of Continuing Education, Panzhihua University, Panzhihua, Sichuan 617000, China

Received 6 December 2013; Accepted 14 January 2014; Published 27 February 2014

Academic Editor: Zhengguang Wu

Copyright © 2014 Yucheng Liu and Yubin Liu. 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. B. Hong, C. H. Hu, and X. P. Jiang, “Iterative multi-step prediction model based on theory of evidence,” Control Theory & Applications, vol. 27, no. 12, pp. 1737–1742, 2010. View at Google Scholar · View at Scopus
  2. G. Hu, Y. Q. Liu, and Y. Q. Li, “The cause of formation, the classification and the control strategy for uncertainty control systems,” Industrial Engineering Journal, vol. 4, no. 1, pp. 49–52, 2001. View at Google Scholar
  3. Y. Yuan, “Uncertain model for knowledge-based system,” Application Research of Computers, vol. 26, no. 9, pp. 3381–3383, 2009. View at Google Scholar
  4. F. Xu, M. Y. Liu, W. B. Li, and X. K. Lei, “Research on digital redesign of the robust controller for parametric uncertain systems,” Systems Engineering and Electronics, vol. 35, no. 1, pp. 156–160, 2013. View at Google Scholar
  5. Y. Dai, S. Shi, and N. Zheng, “Class of robust control strategies for robot manipulators with uncertainties,” Acta Automatica Sinica, vol. 25, no. 2, pp. 204–209, 1999. View at Google Scholar · View at Scopus
  6. P. Carmona, J. L. Castro, and J. M. Zurita, “FRIwE: fuzzy rule identification with exceptions,” IEEE Transactions on Fuzzy Systems, vol. 12, no. 1, pp. 140–151, 2004. View at Publisher · View at Google Scholar · View at Scopus
  7. C. N. Dai, M. Yao, Z. J. Xie, C. H. Chen, and J. G. Liu, “Parameter optimization for growth model of greenhouse crop using genetic algorithms,” Applied Soft Computing Journal, vol. 9, no. 1, pp. 13–19, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. N. S. Bhuvaneswari, G. Uma, and T. R. Rangaswamy, “Adaptive and optimal control of a non-linear process using intelligent controllers,” Applied Soft Computing Journal, vol. 9, no. 1, pp. 182–190, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. Z. P. Liu, Q. Yang, and W. J. Yan, “Intelligent modeling and predictive control of pre-grinding system,” Advanced Materials Research, vol. 433–440, pp. 2120–2127, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. R. Alcalá, J. Alcalá-Fdez, F. Herrera, and J. Otero, “Genetic learning of accurate and compact fuzzy rule based systems based on the 2-tuples linguistic representation,” International Journal of Approximate Reasoning, vol. 44, no. 1, pp. 45–64, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  11. J. González, I. Rojas, H. Pomares et al., “Improving the accuracy while preserving the interpretability of fuzzy function approximators by means of multi-objective evolutionary algorithms,” International Journal of Approximate Reasoning, vol. 44, no. 1, pp. 32–44, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  12. H. Ishibuchi and Y. Nojima, “Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning,” International Journal of Approximate Reasoning, vol. 44, no. 1, pp. 4–31, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  13. E. Van Broekhoven, V. Adriaenssens, and B. De Baets, “Interpretability-preserving genetic optimization of linguistic terms in fuzzy models for fuzzy ordered classification: an ecological case study,” International Journal of Approximate Reasoning, vol. 44, no. 1, pp. 65–90, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  14. K. H. Quah and C. Quek, “FITSK: online local learning with generic fuzzy input Takagi-Sugeno-Kang fuzzy framework for nonlinear system estimation,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 36, no. 1, pp. 166–178, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. S. M. Zhou and J. Q. Gan, “Constructing accurate and parsimonious fuzzy models with distinguishable fuzzy sets based on an entropy measure,” Fuzzy Sets and Systems, vol. 157, no. 8, pp. 1057–1074, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  16. J. Casillas, O. Cordón, M. J. Del Jesus, and F. Herrera, “Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction,” IEEE Transactions on Fuzzy Systems, vol. 13, no. 1, pp. 13–29, 2005. View at Publisher · View at Google Scholar · View at Scopus