Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 531031, 13 pages
http://dx.doi.org/10.1155/2013/531031
Research Article

Artificial Hydrocarbon Networks Fuzzy Inference System

Graduate School of Engineering, Tecnológico de Monterrey, Campus Ciudad de México, 14380 Mexico City, DF, Mexico

Received 13 May 2013; Revised 25 July 2013; Accepted 1 August 2013

Academic Editor: Chen

Copyright © 2013 Hiram Ponce 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. L. Xia and Y. Xiuju, “The application of adaptive fuzzy inference model in the nonlinear dynamic system identificatio,” in Proceedings of the 2nd International Conference on Intelligent Computing Technology and Automation (ICICTA '09), pp. 814–817, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. H. Seki, “Type-2 fuzzy functional SIRMs connected inference model,” in Proceedings of the 6th International Conference on Soft Computing and Intelligent Systems and 13th International Symposium on Advanced Intelligent Systems, pp. 1615–11620, 2012.
  3. K.-E. Ko and K.-B. Sim, “An EEG signals classification system using optimized adaptive neuro-fuzzy inference model based on harmony search algorithm,” in Proceedings of the 11th International Conference on Control, Automation and Systems (ICCAS '11), pp. 1457–1461, October 2011. View at Scopus
  4. Y.-P. Huang, T.-W. Chang, and F.-E. Sandnes, “Improving image retrieval efficiency using a fuzzy inference model and genetic algorithm,” in Proceedings of the Annual Meeting of the North American Fuzzy Information Processing Society, pp. 361–366, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  5. C. Gong and C. Han, “Robust H control of uncertain T-S fuzzy time-delay system: a delay decomposition approach,” Mathematical Problems in Engineering, vol. 2013, Article ID 345601, 10 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  6. W. Huang and S.-K. Oh, “Identification of fuzzy inference systems by means of a multiobjective opposition-based space search algorithm,” Mathematical Problems in Engineering, vol. 2013, Article ID 725017, 13 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  7. T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Transactions on Systems, Man and Cybernetics, vol. 15, no. 1, pp. 116–132, 1985. View at Google Scholar · View at Scopus
  8. E. H. Mamdani, “Application of fuzzy algorithms for control of simple dynamic plant,” IEEE Proceedings of the Institution of Electrical Engineers, vol. 121, no. 12, pp. 1585–1588, 1974. View at Publisher · View at Google Scholar · View at Scopus
  9. Y. Tsukamoto, “An approach to fuzzy reasoning method,” in Advances in Fuzzy Set Theory and Applications, M. Gupta, R. Ragade, and R. Yager, Eds., pp. 137–149, North-Holland, Amsterdam, The Netherlands, 1979. View at Google Scholar · View at MathSciNet
  10. I. Iancu, “A mamdani type fuzzy logic controller,” in Fuzzy Logic: Controls, Concepts, Theories and Applications, pp. 325–350, InTech, 2012. View at Google Scholar
  11. J.-S. R. Jang and C.-T. Sun, “Neuro-fuzzy modeling and control,” Proceedings of the IEEE, vol. 83, no. 3, pp. 378–406, 1995. View at Publisher · View at Google Scholar · View at Scopus
  12. O. Linda and M. Manic, “Comparative analysis of Type-1 and Type-2 fuzzy control in context of learning behaviors for mobile robotics,” in Proceedings of the 36th Annual Conference of the IEEE Industrial Electronics Society (IECON '10), pp. 1092–1098, November 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Musikasuwan and J. M. Garibaldi, “On relationships between primary membership functions and output uncertainties in interval type-2 and non-stationary fuzzy sets,” in Proceedings of the IEEE International Conference on Fuzzy Systems, pp. 1433–1440, July 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. J. M. Mendel and R. I. B. John, “Type-2 fuzzy sets made simple,” IEEE Transactions on Fuzzy Systems, vol. 10, no. 2, pp. 117–127, 2002. View at Publisher · View at Google Scholar · View at Scopus
  15. H. Zhang and D. Liu, Fuzzy Modeling and Fuzzy Control, Springer, Boston, Mass, USA, 2006. View at MathSciNet
  16. H. Ponce and P. Ponce, “Artificial organic networks,” in Proceedings of the IEEE Electronics, Robotics and Automotive Mechanics Conference (CERMA '11), pp. 29–34, November 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. H. Ponce and P. Ponce, “Artificial hydrocarbon networks,” in Proceedings of the 9th International Conference on Innovation and Technological Development (CIINDET '11), pp. 614–618, 2011.
  18. H. Ponce, P. Ponce, and A. Molina, “A novel adaptive filtering for audio signals using artificial hydrocarbon networks,” in Proceedings of the 9th International Conference on Electrical Engineering, Computing Science and Automation Control, pp. 277–282, 2012.
  19. H. Ponce and P. Ponce, “Artificial hydrocarbon networks: a new algorithm bio-inspired on organic chemistry,” International Journal of Artificial Intelligence and Computational Research, vol. 4, no. 1, pp. 39–51, 2012. View at Google Scholar
  20. H. Ponce, P. Ponce, and A. Molina, “A new training algorithm for artificial hydrocarbon networks using an energy model of covalent bonds,” in Proceedings of the IFAC Conference on Manufacturing Modelling, Management and Control, 2013.
  21. H. Ponce, P. Ponce, and A. Molina, “Artificial hydrocarbon networks: a bio-inspired computational algorithm for modeling problems,” Tech. Rep., Graduate School of Engineering, Tecnológico de Monterrey, Campus Ciudad de México, Mexico City, Mexico, 2013. View at Google Scholar
  22. H. Ponce and P. Ponce, “A Novel Approach of Artificial Hydrocarbon Networks on Adaptive Noise Filtering for Audio Signals,” Tech. Rep., Graduate School of Engineering, Tecnológico de Monterrey, Campus Ciudad de México, Mexico City, Mexico, 2013. View at Google Scholar
  23. L. M. de Campos and S. Moral, “Learning rules for a fuzzy inference model,” Fuzzy Sets and Systems, vol. 59, no. 3, pp. 247–257, 1993. View at Publisher · View at Google Scholar · View at MathSciNet
  24. Y.-P. Huang, S.-H. Yu, and M.-S. Horng, “An efficient tuning method for designing a fuzzy inference model,” in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 1–6, October 2001.
  25. K. Ogata, Modern Control Engineering, Prentice Hall, Englewood Cliffs, NJ, USA, 2010.
  26. M. Nie and W. W. Tan, “Towards an efficient type-reduction method for interval type-2 fuzzy logic systems,” in Proceedings of the IEEE International Conference on Fuzzy Systems, pp. 1425–1432, June 2008. View at Publisher · View at Google Scholar · View at Scopus