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International Symposium on Neural Networks

ISNN 2009: Advances in Neural Networks – ISNN 2009 pp 201–207Cite as

Fuzzy Neural Network with a Fuzzy Learning Rule Emphasizing Data Near Decision Boundary

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5552))

Abstract

A fuzzy LVQ (Learning Vector Quantization), which is based on the fuzzification of LVQ, is proposed. This new fuzzy LVQ puts more emphasis on the input vector near decision boundary than on the input vector far from decision boundary. It prevents the outlier from deteriorating the decision boundary. The proposed fuzzy LVQ is used in the neural network, which has the control structure similar to that of ART(Adaptive Resonance Theory)-1 neural network. The result shows that Supervised IAFC (Integrated Adaptive Fuzzy Clustering) Neural Network 6 yielded fewer misclassifications than LVQ algorithm and backpropagation neural network.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Daejeon University, 96-3 Yongun-Dong, Dong-Gu, Daejeon, 300-716, Korea

    Yong Soo Kim

Authors
  1. Yong Soo Kim
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Editor information

Editors and Affiliations

  1. Departamento de Control Automático,, CINVESTAV-IPN,, A.P. 14-740, Av.IPN 2508,, D.F., 07360,, México, México

    Wen Yu

  2. Deptartment of Electrical and Computer Engineering,, Stevens Institute of Technology,, NJ 07030,, Hoboken,, USA

    Haibo He

  3. Dept. of Electrical and Computer Engineering,, South Dakota School of Mines & Technology,, 501 E. St. Joseph Street,, SD 57701,, Rapid City,, USA

    Nian Zhang

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© 2009 Springer-Verlag Berlin Heidelberg

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Kim, Y.S. (2009). Fuzzy Neural Network with a Fuzzy Learning Rule Emphasizing Data Near Decision Boundary. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_23

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  • DOI: https://doi.org/10.1007/978-3-642-01510-6_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01509-0

  • Online ISBN: 978-3-642-01510-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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