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

Authors:
Yong Soo Kim
Affiliations:
Department of Computer Engineering, Daejeon University, Daejeon, Korea 300-716
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Year:
2009

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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.