Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2013, Article ID 745314, 10 pages
http://dx.doi.org/10.1155/2013/745314
Research Article

Design of Polynomial Fuzzy Radial Basis Function Neural Networks Based on Nonsymmetric Fuzzy Clustering and Parallel Optimization

School of Computer and Communication Engineering, Tianjin University of Technology, Tianjin 300384, China

Received 20 April 2013; Revised 24 September 2013; Accepted 8 October 2013

Academic Editor: Jianming Zhan

Copyright © 2013 Wei Huang and Jinsong Wang. 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. S. Mitra and J. Basak, “FRBF: a fuzzy radial basis function network,” Neural Computing and Applications, vol. 10, no. 3, pp. 244–252, 2001. View at Google Scholar · View at Scopus
  2. F. Behloul, B. P. F. Lelieveldt, A. Boudraa, and J. H. C. Reiber, “Optimal design of radial basis function neural networks for fuzzy-rule extraction in high dimensional data,” Pattern Recognition, vol. 35, no. 3, pp. 659–675, 2002. View at Publisher · View at Google Scholar · View at Scopus
  3. S.-K. Oh, W.-D. Kim, W. Pedrycz, and B.-J. Park, “Polynomial-based radial basis function neural networks (P-RBF NNs) realized with the aid of particle swarm optimization,” Fuzzy Sets and Systems, vol. 163, no. 1, pp. 54–77, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  4. W. Huang and L. Ding, “Project-scheduling problem with random time-dependent activity duration times,” IEEE Transactions on Engineering Management, vol. 58, no. 2, pp. 377–387, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. W. Huang and L. Ding, “The shortest path problem on a fuzzy time-dependent network,” IEEE Transactions on Communications, vol. 60, no. 11, pp. 3376–3385, 2012. View at Google Scholar
  6. F.-J. Lin, L.-T. Teng, J.-W. Lin, and S.-Y. Chen, “Recurrent functional-link-based fuzzy-neural-network-controlled induction-generator system using improved particle swarm optimization,” IEEE Transactions on Industrial Electronics, vol. 56, no. 5, pp. 1557–1577, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Alexandridis, H. Sarimveis, and K. Ninos, “A radial basis function network training algorithm using a non-symmetric partition of the input space—application to a model predictive control configuration,” Advances in Engineering Software, vol. 42, no. 10, pp. 830–837, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. A. Alexandridis, E. Chondrodima, and H. Sarimveis, “Radial basis function network training using a nonsymmetric partition of the input space and particle swarm optimization,” IEEE Transactions on Neural Networks and Learning Systems, vol. 42, no. 2, pp. 219–230, 2013. View at Google Scholar
  9. E. Kim, M. Park, S. Ji, and M. Park, “A new approach to fuzzy modeling,” IEEE Transactions on Fuzzy Systems, vol. 5, no. 3, pp. 328–337, 1997. View at Publisher · View at Google Scholar · View at Scopus
  10. Y. Lin and G. A. Cunningham, “A new approach to fuzzy modeling,” IEEE Transactions on Fuzzy Systems, vol. 5, no. 2, pp. 190–197, 1997. View at Google Scholar
  11. S. Oh and W. Pedrycz, “Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems,” Fuzzy Sets and Systems, vol. 115, no. 2, pp. 205–230, 2000. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  12. B. J. Park, S. K. Oh, T. C. Ahn, and H. K. Kim, “Optimization of fuzzy systems by means of GA and weighting factor,” Transactions of the Korean Institute of Electrical Engineers, vol. 48, no. 6, pp. 789–799, 1999. View at Google Scholar
  13. H.-S. Park, B.-J. Park, H.-K. Kim, and S.-K. Oh, “Self-organizing polynomial neural networks based on genetically optimized multi-layer perceptron architecture,” International Journal of Control, Automation and Systems, vol. 2, no. 4, pp. 423–434, 2004. View at Google Scholar · View at Scopus
  14. H.-S. Park and S.-K. Oh, “Fuzzy relation-based fuzzy neural-networks using a hybrid identification algorithm,” International Journal of Control, Automation and Systems, vol. 1, no. 3, pp. 289–300, 2003. View at Google Scholar · View at Scopus
  15. S.-K. Oh, W. Pedrycz, and B.-J. Park, “Relation-based neurofuzzy networks with evolutionary data granulation,” Mathematical and Computer Modelling, vol. 40, no. 7-8, pp. 891–921, 2004. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  16. S. K. Oh and W. Pedrycz, “Fuzzy polynomial neuron-based self-organizing neural networks,” International Journal of General Systems, vol. 32, no. 3, pp. 237–250, 2003. View at Publisher · View at Google Scholar · View at Scopus
  17. S.-K. Oh, H.-S. Park, C.-W. Jeong, and S.-C. Joo, “GA-based feed-forward self-organizing neural network architecture and its applications for multi-variable nonlinear process systems,” KSII Transactions on Internet and Information Systems, vol. 3, no. 3, pp. 309–330, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. W. Pedrycz and K.-C. Kwak, “The development of incremental models,” IEEE Transactions on Fuzzy Systems, vol. 15, no. 3, pp. 507–518, 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. S. K. Oh, W. D. Kim, B. J. Park, and W. Pedrycz, “A design of granular-oriented self-organizing hybrid fuzzy polynomial neural networks,” Neurocomputing, vol. 119, pp. 292–307, 2013. View at Google Scholar
  20. W. Pedrycz and K.-C. Kwak, “Boosting of granular models,” Fuzzy Sets and Systems, vol. 157, no. 22, pp. 2934–2953, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  21. W. Pedrycz, H. S. Park, and S. K. Oh, “A granular-oriented development of functional radial basis function neural networks,” Neurocomputing, vol. 72, no. 1–3, pp. 420–435, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. R. Alcalá, M. J. Gacto, and F. Herrera, “A fast and scalable multiobjective genetic fuzzy system for linguistic fuzzy modeling in high-dimensional regression problems,” IEEE Transactions on Fuzzy Systems, vol. 19, no. 4, pp. 666–681, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. F. Lin and J. Guo, “A novel support vector machine algorithm for solving nonlinear regression problems based on symmetrical points,” in Proceedings of the 2nd International Conference on Computer Engineering and Technology (ICCET '10), pp. 176–180, Chendu, China, April 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. W. Huang, L. Ding, S.-K. Oh, C.-W. Jeong, and S.-C. Joo, “Identification of fuzzy inference system based on information granulation,” KSII Transactions on Internet and Information Systems, vol. 4, no. 4, pp. 575–594, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. F. Hoffmann, “Combining boosting and evolutionary algorithms for learning of fuzzy classification rules,” Fuzzy Sets and Systems, vol. 141, no. 1, pp. 47–58, 2004. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  26. W. Pedrycz and K.-C. Kwak, “Boosting of granular models,” Fuzzy Sets and Systems, vol. 157, no. 22, pp. 2934–2953, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  27. W. Pedrycz and P. Rai, “Collaborative clustering with the use of Fuzzy C-Means and its quantification,” Fuzzy Sets and Systems, vol. 159, no. 18, pp. 2399–2427, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  28. S.-C. Lin, W. F. Punch III, and E. D. Goodman, “Coarse-grain parallel genetic algorithms: categorization and new approach,” in Proceeedings of the 6th IEEE Symposium on Parallel and Distributed Processing, pp. 28–37, Phoenix, Ariz, USA, October 1994. View at Scopus
  29. J. J. Hu and E. D. Goodman, “The hierarchical fair competition HFC model for parallel evolutionary algorithms,” in Proceedings of the Congress on Evolutionary Computation (CEC '02), pp. 45–94, Honolulu, Hawaii, USA, 2002.
  30. J. J. Hu, E. D. Goodman, K. S. Seo, and M. Pei, “Adaptive hierarchical fair competition AHFC model for parallel evolutionary algorithms,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '02), pp. 772–779, New York, NY, USA, 2002.
  31. A. Staiano, R. Tagliaferri, and W. Pedrycz, “Improving RBF networks performance in regression tasks by means of a supervised fuzzy clustering,” Neurocomputing, vol. 69, no. 13–15, pp. 1570–1581, 2006. View at Publisher · View at Google Scholar · View at Scopus
  32. X. Hong and S. Chen, “A new RBF neural network with boundary value constraints,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 39, no. 1, pp. 298–303, 2009. View at Publisher · View at Google Scholar · View at Scopus
  33. C.-M. Huang and F.-L. Wang, “An RBF network with OLS and EPSO algorithms for real-time power dispatch,” IEEE Transactions on Power Systems, vol. 22, no. 1, pp. 96–104, 2007. View at Publisher · View at Google Scholar · View at Scopus
  34. S. K. Oh and W. Pedrycz, “Fuzzy Identification by means of an auto-tuning algorithm and a weighted performance index,” Journal of Fuzzy Logic and Intelligent Systems, vol. 8, pp. 106–118, 1998. View at Google Scholar