DocumentCode :
1917943
Title :
Extension neural network
Author :
Wang, M.H. ; Hung, C.P.
Author_Institution :
Dept. of Electr. Eng., Nat. Chin-Yi Technol. Inst., Taichung, Taiwan
Volume :
1
fYear :
2003
fDate :
20-24 July 2003
Firstpage :
399
Abstract :
A novel extension neural network (ENN) is proposed in this paper. This new neural network is a combination of extension theory and neural network. It uses an extension distance (ED) to measure the similarity between data and cluster center. The learning speed of the proposed ENN is shown to be faster than the traditional neural networks and other fuzzy classification methods. Moreover, the new scheme has been proved to have high accuracy and less memory consumption. Experimental results from the iris data classification problem verify the effectiveness.
Keywords :
fuzzy neural nets; learning (artificial intelligence); pattern classification; cluster center; data center; extension distance; extension neural network; extension theory; fuzzy classification methods; iris data classification problem; Artificial neural networks; Fuzzy neural networks; Fuzzy systems; Iris; Neural networks; Neurons; Pattern recognition; Subspace constraints; Supervised learning; Vector quantization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
ISSN :
1098-7576
Print_ISBN :
0-7803-7898-9
Type :
conf
DOI :
10.1109/IJCNN.2003.1223379
Filename :
1223379
Link To Document :
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