DocumentCode :
2867811
Title :
Improved Fuzzy Single Layer Supervised Learning Algorithm
Author :
Jong-Chan Kim ; Kyeong-Jin Ban ; Eung-kon Kim ; Yang-sun Lee ; An-Suk Oh
Author_Institution :
Dept. of Comput. Eng., Sunchon Nat. Univ., Sunchon, South Korea
fYear :
2011
fDate :
28-30 June 2011
Firstpage :
44
Lastpage :
47
Abstract :
In this paper, we improve the convergence prevented from vibrating decision boundary with bias term and suggest a linear activation function. We propose an enhanced fuzzy single layer perceptron which reduces the learning time introducing the rate of learning and the concept of momentum. We applied to Exclusive OR problem and pattern recognition of letters to analyze the performance of learning through enhanced fuzzy single layer perceptron and precedent fuzzy single layer perceptron. After the number of epoch and the convergence of enhanced fuzzy single layer perceptron were compared with those of precedent one, we found that enhanced one had far less time for learning and improved the convergence.
Keywords :
fuzzy set theory; learning (artificial intelligence); pattern recognition; perceptrons; bias term; exclusive OR problem; linear activation function; pattern recognition; precedent fuzzy single layer perceptron enhancement; supervised learning algorithm; Artificial neural networks; Classification algorithms; Convergence; Fuzzy logic; Learning systems; Pattern classification; Pattern recognition; bias term; fuzzy function; fuzzy single layer perceptron; learning rate; momentum;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia and Ubiquitous Engineering (MUE), 2011 5th FTRA International Conference on
Conference_Location :
Loutraki
Print_ISBN :
978-1-4577-1228-9
Electronic_ISBN :
978-0-7695-4470-0
Type :
conf
DOI :
10.1109/MUE.2011.19
Filename :
5992169
Link To Document :
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