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http://dx.doi.org/10.5391/JKIIS.2005.15.7.800

Fuzzy Neural Network Model Using Asymmetric Fuzzy Learning Rates  

Kim Yong-Soo (대전대학교 컴퓨터공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.15, no.7, 2005 , pp. 800-804 More about this Journal
Abstract
This paper presents a fuzzy learning rule which is the fuzzified version of LVQ(Learning Vector Quantization). This fuzzy learning rule 3 uses fuzzy learning rates. instead of the traditional learning rates. LVQ uses the same learning rate regardless of correctness of classification. But, the new fuzzy learning rule uses the different learning rates depending on whether classification is correct or not. The new fuzzy learning rule is integrated into the improved IAFC(Integrated Adaptive Fuzzy Clustering) neural network. The improved IAFC neural network is both stable and plastic. The iris data set is used to compare the performance of the supervised IAFC neural network 3 with the performance of backprogation neural network. The results show that the supervised IAFC neural network 3 is better than backpropagation neural network.
Keywords
Learning Vector Quantization; fuzzification; fuzzy learning rule; supervised IAFC neural network 3;
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