A Knowledge Discovery by Fuzzy Rule Based Hopfield Network

  • Thanakorn Sornkaew
  • Yasuo Yamashita
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2412)


In this paper, a new method for discovering knowledge from empirical data is proposed. This model consists of five steps. Firstly, we find the centers of fuzzy membership functions using adapted self-organizing feature map (SOFM). Secondly, we use the centers of Gaussian membership functions derived from previous step to determine the widths of Gaussian membership functions by means of the first-nearest-neighbor heuristic. Thirdly, it builds a weight network of Hopfield network so that weights reflect the importance of the network’s connections. Fourthly, Hopfield network is operated to get output values. The final step is to extract rules or knowledge via our proposed algorithm. In this algorithm, the irrelevant candidate rules are deleted so that the number of fuzzy rules and the number of antecedents can be defined. Therefore, it extracts fuzzy rules from the network. The experiments on wine recognition data show good performance concerning predictive accuracy.


Membership Function Fuzzy Rule Knowledge Discovery Synaptic Weight Fuzzy Membership Function 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Thanakorn Sornkaew
    • 1
  • Yasuo Yamashita
    • 1
  1. 1.Department of Industrial Engineering and ManagementNihon UniversityChibaJapan

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