Supervised IAFC Neural Network Based on the Fuzzification of Learning Vector Quantization

  • Yong Soo Kim
  • Sang Wan Lee
  • Sukhoon Kang
  • Yong Sun Baek
  • Suntae Hwang
  • Zeungnam Bien
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4253)


In this paper, a fuzzy LVQ(Learning Vector Quantization) is proposed which is based on the fuzzification of LVQ. The proposed FLVQ(Fuzzy Learning Vector Quantization) uses the different learning rate depending on the correctness of classification. When the classification is correct, the amount of update is determined by consideration of location of the input vector relative to the decision boundary. When the classification is not correct, the amount of update is determined by the degree of belongingness of the input vector to the winning class. The supervised IAFC(Integrated Adaptive Fuzzy Clustering) neural network 3, which uses FLVQ, is introduced in this paper. The supervised IAFC neural network 3 is both stable and plastic because it uses the control structure which is similar to that of Adaptive Resonance Theory(ART)-1 neural network. We used iris data set to compare the performance of the supervised IAFC neural network 3 with those of LVQ algorithm and backpropagation neural network. The supervised IAFC neural network 3 yielded fewer misclassifications than LVQ algorithm and backpropa-gation neural network.


Input Vector Output Neuron Fuzzy Membership Decision Boundary Learn Vector Quantization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Lin, C.-T., Lee, C.S.G.: Neural Fuzzy Systems – A Neuro-Fuzzy Synergism to Intelligent Systems. Prentice-Hall, New Jersey (1996)Google Scholar
  2. 2.
    Bezdek, J.C., Tsao, E.C., Pal, N.R.: Fuzzy Kohonen Clustering Networks. In: Proceeding of the First IEEE conference on Fuzzy Systems, San Diego, pp. 1035–1043 (1992)Google Scholar
  3. 3.
    Chung, F.-L., Lee, T.: A fuzzy Learning Model for Membership Function Estimation and Pattern Classification. In: Proceedings of the third IEEE Conference on Fuzzy Systems, vol. 1, pp. 426–431 (1994)Google Scholar
  4. 4.
    Chung, F.-L., Lee, T.: Fuzzy Learning Vector Quantization. In: Proceedings of 1993 International Joint Conference on Neural Networks, Nagoya, vol. 3, pp. 2739–2743 (1993)Google Scholar
  5. 5.
    Karayiannis, N.B.: Weighted Fuzzy Learning Vector Quantization and Weighted Fuzzy C-Means Algorithms. In: IEEE International Conference on Neural Networks, vol. 2, pp. 1044–1049 (1996)Google Scholar
  6. 6.
    Karayiannis, N.B., Bezdek, J.C.: An Integrated Approach to Fuzzy Learning Vector Quantization and Fuzzy C-Means Clustering. IEEE Transactions on Fuzzy Systems 5, 629–662 (1997)CrossRefGoogle Scholar
  7. 7.
    Tsao, E.C.-K., Bezdek, J.C., Pal, N.R.: Fuzzy Kohonen Clustering Networks. Pattern Recognition 27(5), 757–764 (1994)CrossRefGoogle Scholar
  8. 8.
    Carpenter, G.A., Grossberg, S.: A Massively Parallel Architecture for A Self-Organizing Neural Pattern Recognition Machine. Computer Vision, Graphics, and Image Processing 37, 54–115 (1987)CrossRefGoogle Scholar
  9. 9.
    Kim, Y.S., Mitra, S.: An adaptive integrated fuzzy clustering model for pattern recognition. Fuzzy Sets and Systems 65, 297–310 (1994)CrossRefGoogle Scholar
  10. 10.
    Moore, B.: ART1 and Pattern Clustering. In: Proceedings of the 1988 Connectionist Models Summer School, San Mateo, pp. 174–185 (1989)Google Scholar
  11. 11.
    Tou, J.T., Gonzalez, R.C.: Pattern Recognition Principles. Addison-Wesley, Massachsetts (1974)MATHGoogle Scholar
  12. 12.
    Anderson, E.: The IRISes of the Gaspe Penninsula. Bulletin American IRIS Society 59, 2–5 (1935)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yong Soo Kim
    • 1
  • Sang Wan Lee
    • 2
  • Sukhoon Kang
    • 1
  • Yong Sun Baek
    • 3
  • Suntae Hwang
    • 4
  • Zeungnam Bien
    • 2
  1. 1.Department of Computer EngineeringDaejeon UniversityDaejeonKorea
  2. 2.Department of Electrical Engineering and Computer Science, KAISTDaejeonKorea
  3. 3.Department of Computer Web InformationDaeduk CollegeDaejeonKorea
  4. 4.Department of Information and Communications EngineeringDaejeon UniversityDaejeonKorea

Personalised recommendations