Table of Contents
Advances in Artificial Neural Systems
Volume 2011 (2011), Article ID 107498, 10 pages
http://dx.doi.org/10.1155/2011/107498
Research Article

A Novel Learning Scheme for Chebyshev Functional Link Neural Networks

Department of Information and Communication Technology, Fakir Mohan University, Vyasa Vihar, Balasore, Orissa 756019, India

Received 11 January 2011; Revised 17 April 2011; Accepted 28 May 2011

Academic Editor: Giacomo Indiveri

Copyright © 2011 Satchidananda Dehuri. 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. Ghosh and Y. Shin, “Efficient higher-order neural networks for classification and function approximation,” International Journal of Neural Systems, vol. 3, pp. 323–350, 1992. View at Google Scholar
  2. S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, Englewood Cliffs, NJ, USA, 1999.
  3. O. L. Mangasarian and E. W. Wild, “Nonlinear knowledge-based classification,” IEEE Transactions on Neural Networks, vol. 19, no. 10, pp. 1826–1832, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. K. Hornik, “Approximation capabilities of multilayer feedforward networks,” Neural Networks, vol. 4, no. 2, pp. 251–257, 1991. View at Google Scholar · View at Scopus
  5. C. L. Giles and T. Maxwell, “Learning, invariance, and generalization in high-order neural networks,” Applied Optics, vol. 26, no. 23, pp. 4972–4978, 1987. View at Google Scholar · View at Scopus
  6. Y. H. Pao, Adaptive Pattern Recognition and Neural Network, Addison-Wesley, Reading, Mass, USA, 1989.
  7. E. Artyomov and O. Yadid-Pecht, “Modified high-order neural network for invariant pattern recognition,” Pattern Recognition Letters, vol. 26, no. 6, pp. 843–851, 2005. View at Publisher · View at Google Scholar · View at Scopus
  8. B. B. Misra and S. Dehuri, “Functional link neural network for classification task in data mining,” Journal of Computer Science, vol. 3, no. 12, pp. 948–955, 2007. View at Google Scholar
  9. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Morgan Kaufmann, 1989.
  10. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, Pisacataway, NJ, USA, December 1995. View at Scopus
  11. J. D. Schaffer, D. Whitley, and L. J. Eshelman, “Combinations of genetic algorithms and neural networks: a survey of the state of the art,” in Proceedings of International Workshop on Combinations of Genetic Algorithms and Neural Networks, pp. 1–37, 1992.
  12. Y. Davidor, “Epistasis variance: suitability of a representation to genetic algorithms,” Complex Systems, vol. 4, pp. 368–383, 1990. View at Google Scholar
  13. L. J. Eshelman and J. D. Schaffer, “Real coded genetic algorithms and interval schemata,” in Foundation of Genetic Algorithms, L. D. Whitley, Ed., pp. 187–202, Morgan Kaufmann, 1993. View at Google Scholar
  14. H. Muhlenbein and D. Schlierkamp-Voosen, “Predictive models for the breeder genetic algorithm I. Continuous parameters optimization,” Evolutionary Computation, vol. 1, no. 1, pp. 24–49, 1993. View at Google Scholar
  15. J. F. Schutte and A. A. Groenwold, “A study of global optimization using particle swarms,” Journal of Global Optimization, vol. 31, no. 1, pp. 93–108, 2005. View at Publisher · View at Google Scholar · View at Scopus
  16. M. M. Ali and P. Kaelo, “Improved particle swarm algorithms for global optimization,” Applied Mathematics and Computation, vol. 196, no. 2, pp. 578–593, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. J. Yu, S. Wang, and L. Xi, “Evolving artificial neural networks using an improved PSO and DPSO,” Neurocomputing, vol. 71, no. 4–6, pp. 1054–1060, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. Y. Da and X. R. Ge, “An improved PSO-based ANN with simulated annealing technique,” Neurocomputing, vol. 63, pp. 527–533, 2005. View at Publisher · View at Google Scholar · View at Scopus
  19. M. S. Klassen, Y. H. Pao, and V. Chen, “Characteristics of the functional link net: a higher order delta rule net,” in Proceedings of the 2nd Annual International Conference on Neural Networks, vol. 1, pp. 507–513, San Diago, Calif, USA, 1988.
  20. Y. H. Pao and Y. Takefuji, “Functional-link net computing: theory, system architecture, and functionalities,” Computer, vol. 25, no. 5, pp. 76–79, 1992. View at Publisher · View at Google Scholar · View at Scopus
  21. A. Sierra, J. A. Macías, and F. Corbacho, “Evolution of functional link networks,” IEEE Transactions on Evolutionary Computation, vol. 5, no. 1, pp. 54–65, 2001. View at Publisher · View at Google Scholar · View at Scopus
  22. B. Haring and J. N. Kok, “Finding functional links for neural networks by evolutionary computation,” in Proceedings of the 5th Belgian-Dutch Conference on Machine Learning (BENELEARN '95), T. Van de Merckt et al., Ed., pp. 71–78, Brussels, Belgium, 1995.
  23. J. C. Patra and R. N. Pal, “A functional link artificial neural network for adaptive channel equalization,” Signal Processing, vol. 43, no. 2, pp. 181–195, 1995. View at Google Scholar · View at Scopus
  24. S. Haring, J. N. Kok, and M. C. van Wezel, “Feature selection for neural networks through functional links found by evolutionary computation,” in Proceedings of the 2nd International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data, X. Liu et al., Ed., vol. 1280 of Lecture Notes in Computer Science, pp. 199–210, 1997.
  25. P. K. Dash, A. C. Liew, and H. P. Satpathy, “A functional-link-neural network for short-term electric load forecasting,” Journal of Intelligent and Fuzzy Systems, vol. 7, no. 3, pp. 209–221, 1999. View at Google Scholar · View at Scopus
  26. D. A. Panagiotopoulos, R. W. Newcomb, and S. K. Singh, “Planning with a functional neural-network architecture,” IEEE Transactions on Neural Networks, vol. 10, no. 1, pp. 115–127, 1999. View at Google Scholar · View at Scopus
  27. J. C. Patra, R. N. Pal, B. N. Chatterji, and G. Panda, “Identification of nonlinear dynamic systems using functional link artificial neural networks,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 29, no. 2, pp. 254–262, 1999. View at Publisher · View at Google Scholar · View at Scopus
  28. J. C. Patra and A. van den Bos, “Modeling of an intelligent pressure sensor using functional link artificial neural networks,” ISA Transactions, vol. 39, no. 1, pp. 15–27, 2000. View at Google Scholar · View at Scopus
  29. J. C. Patra and A. C. Kot, “Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 32, no. 4, pp. 505–511, 2002. View at Publisher · View at Google Scholar · View at Scopus
  30. S. N. Singh and K. N. Srivastava, “Degree of insecurity estimation in a power system using functional link neural network,” European Transactions on Electrical Power, vol. 12, no. 5, pp. 353–358, 2002. View at Publisher · View at Google Scholar · View at Scopus
  31. T. Marcu and B. Koppen-Seliger, “Dynamic functional link neural networks genetically evolved applied to system identification,” in Proceedings of European Symposium on Artificial Neural Networks (ESANN '04), pp. 115–120, Bruges, Belgium, 2004.
  32. B. Majhi and H. Shalabi, “An improved scheme for digital watermarking using functional link artificial neural network,” Journal of Computer Science, vol. 1, no. 2, pp. 169–174, 2005. View at Google Scholar
  33. I. A. Abu-Mahfouz, “A comparative study of three artificial neural networks for the detection and classification of gear faults,” International Journal of General Systems, vol. 34, no. 3, pp. 261–277, 2005. View at Publisher · View at Google Scholar · View at Scopus
  34. S. Purwar, I. N. Kar, and A. N. Jha, “On-line system identification of complex systems using Chebyshev neural networks,” Applied Soft Computing Journal, vol. 7, no. 1, pp. 364–372, 2007. View at Publisher · View at Google Scholar · View at Scopus
  35. W. D. Weng, C. S. Yang, and R. C. Lin, “A channel equalizer using reduced decision feedback Chebyshev functional link artificial neural networks,” Information Sciences, vol. 177, no. 13, pp. 2642–2654, 2007. View at Publisher · View at Google Scholar · View at Scopus
  36. D. Krishnaiah, D. M. R. Prasad, A. Bono, P. M. Pandiyan, and R. Sarbatly, “Application of ultrasonic waves coupled with functional link neural network for estimation of carrageenan concentration,” International Journal of Physical Sciences, vol. 3, no. 4, pp. 90–96, 2008. View at Google Scholar
  37. J. C. Patra, G. Chakraborty, and S. Mukhopadhyay, “Functional link neural network-based intelligent sensors for harsh environments,” Sensors & Transducers Journal, vol. 90, pp. 209–220, 2008. View at Google Scholar
  38. S. Dehuri, B. B. Mishra, and S.-B. Cho, “Genetic feature selection for optimal functional link artificial neural network in classification,” in Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL '08), C. Fyfe et al., Ed., vol. 5326 of Lecture Notes in Computer Science, pp. 156–163, 2008. View at Publisher · View at Google Scholar
  39. Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC '98), pp. 69–73, IEEE Press, Pisacataway, NJ, USA, May 1998. View at Scopus
  40. Y. Shi and R. C. Eberhart, “Parameter selection in particle swarm optimization,” in Evolutionary Programming VII, vol. 1447 of Lecture Notes in Computer Science, pp. 591–600, Springer, Berlin, Germany, 1998. View at Google Scholar
  41. P. C. Fourie and A. A. Groenwold, “The particle swarm optimization algorithm in size and shape optimization,” Structural and Multidisciplinary Optimization, vol. 23, no. 4, pp. 259–267, 2002. View at Publisher · View at Google Scholar · View at Scopus
  42. M. Clerc and J. Kennedy, “The particle swarm-explosion, stability, and convergence in a multidimensional complex space,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, pp. 58–73, 2002. View at Publisher · View at Google Scholar · View at Scopus
  43. J. R. Zhang, J. Zhang, T. M. Lok, and M. R. Lyu, “A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training,” Applied Mathematics and Computation, vol. 185, no. 2, pp. 1026–1037, 2007. View at Publisher · View at Google Scholar · View at Scopus
  44. A. Ratnaweera, S. K. Halgamuge, and H. C. Watson, “Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 240–255, 2004. View at Publisher · View at Google Scholar · View at Scopus
  45. C. L. Blake and C. J. Merz, “UCI repository of machine learning databases,” http://www.ics.uci.edu/ mlearn/MLRepository.html.
  46. R. P. Lippmann, “An introduction to computing with neural networks,” IEEE ASSP Magazine, vol. 4, no. 2, pp. 4–22, 1987. View at Google Scholar · View at Scopus
  47. L. Preshelt, “Proben1-a set of neural network benchmark problems and benchmarking rules,” Tech. Rep. 21/94, Universitat Karlsruhe, Karlsruhe, Germany, 1994. View at Google Scholar