Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 970931, 8 pages
http://dx.doi.org/10.1155/2014/970931
Research Article

Multilayer Perceptron for Robust Nonlinear Interval Regression Analysis Using Genetic Algorithms

Department of Business Administration, Chung Yuan Christian University, Chung Li 32023, Taiwan

Received 5 May 2014; Accepted 12 June 2014; Published 29 June 2014

Academic Editor: Chih-Chou Chiu

Copyright © 2014 Yi-Chung Hu. 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. C. Hwang, D. H. Hong, and K. H. Seok, “Support vector interval regression machine for crisp input and output data,” Fuzzy Sets and Systems, vol. 157, no. 8, pp. 1114–1125, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  2. J. T. Jeng, C. C. Chuang, and S. F. Su, “Support vector interval regression networks for interval regression analysis,” Fuzzy Sets and Systems, vol. 138, no. 2, pp. 283–300, 2003. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  3. L. Huang, B. Zhang, and Q. Huang, “Robust interval regression analysis using neural networks,” Fuzzy Sets and Systems, vol. 97, no. 3, pp. 337–347, 1998. View at Publisher · View at Google Scholar · View at Scopus
  4. H. Tanaka, S. Uejima, and K. Asai, “Linear regression analysis with fuzzy model,” IEEE Transactions on Systems, Man and Cybernetics, vol. 12, no. 6, pp. 903–907, 1982. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Watada and Y. Yabuuchi, “Fuzzy robust regression analysis,” in Proceedings of the 3rd IEEE Conference on Fuzzy Systems, pp. 1370–1376, Beijing, China, June 1994. View at Scopus
  6. H. Ishibuchi and H. Tanaka, “Fuzzy regression analysis using neural networks,” Fuzzy Sets and Systems, vol. 50, no. 3, pp. 257–265, 1992. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  7. H. Ishibuchi, H. Tanaka, and H. Okada, “An architecture of neural networks with interval weights and its application to fuzzy regression analysis,” Fuzzy Sets and Systems, vol. 57, no. 1, pp. 27–39, 1993. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  8. H. Ishibuchi and M. Nii, “Fuzzy regression using asymmetric fuzzy coefficients and fuzzified neural networks,” Fuzzy Sets and Systems, vol. 119, no. 2, pp. 273–290, 2001. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  9. C. B. Cheng and E. S. Lee, “Fuzzy regression with radial basis function network,” Fuzzy Sets and Systems, vol. 119, no. 2, pp. 291–301, 2001. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  10. K. Kwon, H. Ishibuchi, and H. Tanaka, “Neural networks with interval weights for nonlinear mappings of interval vectors,” IEICE Transactions on Information and Systems, vol. E77-D, no. 4, pp. 409–417, 1994. View at Google Scholar · View at Scopus
  11. H. Ishibuchi and H. Tanaka, “Several formulations of interval regression analysis,” in Proceedings of the Sino-Japan Joint Meeting on Fuzzy Sets and Systems, Beijing, China, 1990.
  12. D. S. Chen and R. C. Jain, “Robust back propagation learning algorithm for function approximation,” IEEE Transactions on Neural Networks, vol. 5, no. 3, pp. 467–479, 1994. View at Publisher · View at Google Scholar · View at Scopus
  13. Y. C. Hu, “Functional-link nets with genetic-algorithm-based learning for robust nonlinear interval regression analysis,” Neurocomputing, vol. 72, no. 7–9, pp. 1808–1816, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, Pa, USA, 1989.
  15. J. S. R. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hall, Upper Saddle River, NJ, USA, 1997.
  16. K. A. Smith and J. N. D. Gupta, “Neural networks in business: techniques and applications for the operations researcher,” Computers and Operations Research, vol. 27, no. 11-12, pp. 1023–1044, 2000. View at Publisher · View at Google Scholar · View at Scopus
  17. G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Mathematics of Control, Signals, and Systems, vol. 2, no. 4, pp. 303–314, 1989. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  18. A. Osyczka, Evolutionary Algorithms for Single and Multicriteria Design Optimization, Physica, New York, NY, USA, 2002.
  19. K. F. Man, K. S. Tang, and S. Kwong, Genetic Algorithms: Concepts and Designs, Springer, London, UK, 1999. View at Publisher · View at Google Scholar · View at MathSciNet
  20. D. Chwirut, Ultrasonic Reference Block Study Data, National Institute of Standards and Technology (NIST), US Department of Commerce, Gaithersburg, Md, USA, 1979.
  21. L. Roszman, Quantum defects for sulfur I atom, National Institute of Standards and Technology (NIST), US Department of Commerce, 1979, http://www.itl.nist.gov/div898/strd/nls/data/roszman1.shtml.
  22. R. A. A. Fagundes, R. M. C. R. de Souza, and F. J. A. Cysneiros, “Robust regression with application to symbolic interval data,” Engineering Applications of Artificial Intelligence, vol. 26, no. 1, pp. 564–573, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. C. Chuang and Z. Lee, “Hybrid robust support vector machines for regression with outliers,” Applied Soft Computing Journal, vol. 11, no. 1, pp. 64–72, 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. P. D'Urso, R. Massari, and A. Santoro, “Robust fuzzy regression analysis,” Information Sciences, vol. 181, no. 19, pp. 4154–4174, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  25. C. Huang, “A reduced support vector machine approach for interval regression analysis,” Information Sciences, vol. 217, pp. 56–64, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus