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http://dx.doi.org/10.5391/JKIIS.2006.16.4.460

Fuzzy Neural Network Model Using A Learning Rule Considering the Distances Between Classes  

Kim Yong-Soo (대전대학교 컴퓨터공학과)
Baek Yong-Sun (대덕대학 컴퓨터웹정보과)
Lee Se-Yul (청운대학교 컴퓨터학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.16, no.4, 2006 , pp. 460-465 More about this Journal
Abstract
This paper presents a new fuzzy learning rule which considers the Euclidean distances between the input vector and the prototypes of classes. The new fuzzy learning rule is integrated into the supervised IAFC neural network 4. This neural network is stable and plastic. We used iris data to compare the performance of the supervised IAFC neural network 4 with the performances of back propagation neural network and LVQ algorithm.
Keywords
Learning rule; Fuzzy vector quantization; Supervised IAFC neural network 4; Euclidean distance; Decision boundary;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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