International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Finding Fuzzy Rules for IRIS by Neural Network with Weighted Fuzzy Membership Function

  • Published : 2004.09.01
Download PDF
⟨ Previous Next ⟩

Abstract

Fuzzy neural networks have been successfully applied to analyze/generate predictive rules for medical or diagnostic data. However, most approaches proposed so far have not considered the weights for the membership functions much. This paper presents a neural network with weighted fuzzy membership functions. In our approach, the membership functions can capture the concentrated and essential information that affects the classification of the input patterns. To verify the performance of the proposed model, well-known Iris data set is performed. According to the results, the weighted membership functions enhance the prediction accuracy. The architecture of the proposed neural network with weighted fuzzy membership functions and the details of experimental results for the data set is discussed in this paper.

Keywords

References

  1. C.L Blake and C.J. Merz, 'UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/ML Repository. html]', Irvine, CA: University of California, Department of Information and Computer Science, 1998
  2. G. A. Carpenter, S. Grossberg, and J. H. Reynolds, 'ARTMAP: Supervised realtime learning and classification of nonstationary data by a self-organizing neural network,' Neural Networks, Vol. 4, pp. 565-588, 1991 https://doi.org/10.1016/0893-6080(91)90012-T
  3. R.O. Duda and P.E. Hart, 'Pattern Classification and Scene Analysis', New York: Wiley, 1973
  4. R. A. Fisher, 'The Use of Multiple Measurements in taxonomic Problem,' Annals of Eugenics, Vol. 7, No.2, pp.179-188, 1936 https://doi.org/10.1111/j.1469-1809.1936.tb02137.x
  5. A. F. Gomez-Skarmeta, M. V. F. Jimenez, J. G. MarinBlazques, 'Approximative Fuzzy Rules Approaches for Classification with Hybrid-GA Techniques,' Infonnation Sciences, Vol. 136, pp.193-214, 2001 https://doi.org/10.1016/S0020-0255(01)00148-7
  6. S. Haykin, 'Neural Networks, a comprehensive foundation', Prentice Hall, New Jersey, 1999
  7. H. Ishibuchi and T. Nakashima, 'Voting in Fuzzy Rule-Based Systems for Pattern Classification Problems,' Fuzzy Sets and Systems, Vol. 103, pp. 223-238, 1999 https://doi.org/10.1016/S0165-0114(98)00223-1
  8. R. Jang, 'ANFIS: Adaptive network-based fuzzy inference system,' IEEE Trans. Syst., Man, Cybern., Vol. 23, pp. 665-685, May-June 1993 https://doi.org/10.1109/21.256541
  9. C. F. Juang and C. T. Lin, 'An On-Line Self-Constructing Neural Fuzzy Inference Network and Its Applications,' IEEE Trans. Fuzzy Systems, Vol. 6(1), pp. 12-32, 1998 https://doi.org/10.1109/91.660805
  10. N. Kasabov, Foundation of Neural Networks, Fuzzy Systems and Knowledge Engineering, The MIT Press, Cambridge, MA, 1996
  11. T. Kasuba, 'Simplified Fuzzy ARTMAP,' IEEE AI Expert, pp. 19-25, Nov. 1993
  12. B. Kosko, Neural networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, Englewood Cliffs, NJ:Prentice-Hall, 1992
  13. H.-M. Lee, K.-H. Chen, and I-F. Jiang, 'A Neural Networks with Disjunctive Fuzzy Information,' Neural Networks, Vol. 11, pp. 1113-1125, 1998 https://doi.org/10.1016/S0893-6080(98)00058-6
  14. C. T. Lin and C. S. George Lee, 'Neural-network-based fuzzy logic control and decision system,' IEEE Trans. Computers, Vol. 40, No. 12, Dec., 1991
  15. D. Nauck and R Kruse, 'A Neuro-Fuzzy Method to Learn Fuzzy Classification Rules from Data,' Fuzzy Sets and Systems, Vol. 89, pp. 277-288, 1997 https://doi.org/10.1016/S0165-0114(97)00009-2
  16. M. Setnes and H. Roubos, 'GA-Fuzzy Modeling and Classification: Complexity and Perfonnance,' IEEE Trans. Fuzzy Systems, Vol. 8(5), pp. 509-522, 2000 https://doi.org/10.1109/91.873575
  17. P. Simpson, 'Fuzzy min-max neural networks-Part 1: Classification,' IEEE Trans. Neural Networks, Vol. 3, pp. 776-786, 1992 https://doi.org/10.1109/72.159066
  18. T. Takagi and M. Sugeno, 'Fuzzy Identification of Systems and Its Application to Modeling and Control,' IEEE Trans. Syst., Man, Cybern., Vol. 15, pp. 116-132, 1985
  19. K. Tanaka, M. Sano, and H. Watanabe, 'Modeling and Control of Carbon Monoxide Concentration Using a Neuro-Fuzzy technique,' IEEE Trans. Fuzzy Systems, Vol. 3, pp. 271-279, June, 1995 https://doi.org/10.1109/91.413233
  20. C. Z. Ye, J. Yang, D. Y. Geng, Y. Zhou, N. Y. Chen, Fuzzy Rules to Predict Degree of Malignancy in Brain Glioma,' Medical and Biological Engineering and Computing, Vol.40, 2002
  21. J. S. Wang and C. S. G. Lee, 'Self-Adaptive Neuro-Fuzzy Inference System for Classification Applications,' IEEE Trans. Fuzzy Systems, Vol. 10(6), pp. 790-802, 2002 https://doi.org/10.1109/TFUZZ.2002.805880
  22. W. Wolberg, O. Mangasarian, 'Multisurface Method of Pattern . for Medical Diagnosis Applied to Breast Cytology,' Proc. National Academy of Sciences, Vol.87, pp. 9193-9166, 1990 https://doi.org/10.1073/pnas.87.23.9193
  23. L. Zadeh, 'Fuzzy sets. Information and Control,'Vol. 8, pp. 338-353, 1965. E. E. Reber, R. L. Michell, and C. J. Carter, 'Oxygen absorption in the Earth's atmosphere,' Aerospace Corp., Los Angeles, CA, Tech. Rep. TR-0200 (420-46)-3, Nov. 1988 https://doi.org/10.1016/S0019-9958(65)90241-X

Cited by

  1. Minimized Stock Forecasting Features Selection by Automatic Feature Extraction Method vol.19, pp.2, 2009, https://doi.org/10.5391/JKIIS.2009.19.2.206