Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lin, C.-T., Lee, C.S.G.: Neural Fuzzy Systems – A Neuro-Fuzzy Synergism to Intelligent Systems. Prentice-Hall, New Jersey (1996)
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)
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)
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)
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)
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)
Tsao, E.C.-K., Bezdek, J.C., Pal, N.R.: Fuzzy Kohonen Clustering Networks. Pattern Recognition 27(5), 757–764 (1994)
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)
Kim, Y.S., Mitra, S.: An adaptive integrated fuzzy clustering model for pattern recognition. Fuzzy Sets and Systems 65, 297–310 (1994)
Moore, B.: ART1 and Pattern Clustering. In: Proceedings of the 1988 Connectionist Models Summer School, San Mateo, pp. 174–185 (1989)
Tou, J.T., Gonzalez, R.C.: Pattern Recognition Principles. Addison-Wesley, Massachsetts (1974)
Anderson, E.: The IRISes of the Gaspe Penninsula. Bulletin American IRIS Society 59, 2–5 (1935)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, Y.S., Lee, S.W., Kang, S., Baek, Y.S., Hwang, S., Bien, Z. (2006). Supervised IAFC Neural Network Based on the Fuzzification of Learning Vector Quantization. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893011_32
Download citation
DOI: https://doi.org/10.1007/11893011_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46542-3
Online ISBN: 978-3-540-46544-7
eBook Packages: Computer ScienceComputer Science (R0)