Browse > Article
http://dx.doi.org/10.5391/IJFIS.2004.4.2.211

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

Lim, Joon Shik (College of Software, Kyungwon University)
Publication Information
International Journal of Fuzzy Logic and Intelligent Systems / v.4, no.2, 2004 , pp. 211-216 More about this Journal
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
Fuzzy neural network; rule extraction; weighted membership function;
Citations & Related Records
연도 인용수 순위
  • Reference
1 S. Haykin, 'Neural Networks, a comprehensive foundation', Prentice Hall, New Jersey, 1999
2 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   DOI   ScienceOn
3 B. Kosko, Neural networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, Englewood Cliffs, NJ:Prentice-Hall, 1992
4 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
5 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   DOI   ScienceOn
6 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
7 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   DOI
8 R.O. Duda and P.E. Hart, 'Pattern Classification and Scene Analysis', New York: Wiley, 1973
9 M. Setnes and H. Roubos, 'GA-Fuzzy Modeling and Classification: Complexity and Perfonnance,' IEEE Trans. Fuzzy Systems, Vol. 8(5), pp. 509-522, 2000   DOI   ScienceOn
10 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   DOI   ScienceOn
11 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   DOI   ScienceOn
12 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
13 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
14 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   DOI   ScienceOn
15 N. Kasabov, Foundation of Neural Networks, Fuzzy Systems and Knowledge Engineering, The MIT Press, Cambridge, MA, 1996
16 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   DOI   ScienceOn
17 T. Kasuba, 'Simplified Fuzzy ARTMAP,' IEEE AI Expert, pp. 19-25, Nov. 1993
18 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   DOI   ScienceOn
19 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   DOI   ScienceOn
20 R. A. Fisher, 'The Use of Multiple Measurements in taxonomic Problem,' Annals of Eugenics, Vol. 7, No.2, pp.179-188, 1936   DOI
21 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   DOI   ScienceOn
22 P. Simpson, 'Fuzzy min-max neural networks-Part 1: Classification,' IEEE Trans. Neural Networks, Vol. 3, pp. 776-786, 1992   DOI   ScienceOn
23 R. Jang, 'ANFIS: Adaptive network-based fuzzy inference system,' IEEE Trans. Syst., Man, Cybern., Vol. 23, pp. 665-685, May-June 1993   DOI   ScienceOn