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http://dx.doi.org/10.5391/JKIIS.2007.17.4.472

Fuzzy Learning Rule Using the Distance between Datum and the Centroids of Clusters  

Kim, Yong-Soo (대전대학교 컴퓨터공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.17, no.4, 2007 , pp. 472-476 More about this Journal
Abstract
Learning rule affects importantly the performance of neural network. This paper proposes a new fuzzy learning rule that uses the learning rate considering the distance between the input vector and the prototypes of classes. When the learning rule updates the prototypes of classes, this consideration reduces the effect of outlier on the prototypes of classes. This comes from making the effect of the input vector, which locates near the decision boundary, larger than an outlier. Therefore, it can prevents an outlier from deteriorating the decision boundary. This new fuzzy learning rule is integrated into IAFC(Integrated Adaptive Fuzzy Clustering) fuzzy neural network. Iris data set is used to compare the performance of the proposed fuzzy neural network with those of other supervised neural networks. The results show that the proposed fuzzy neural network is better than other supervised neural networks.
Keywords
Learning rates; Learning rule; IAFC neural network; Fuzzy Learning Vector Quantization;
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