DocumentCode
1648988
Title
Pattern recognition with block-based neural networks
Author
Moon, Sang-Woo ; Kong, Seong-Gon
Author_Institution
Dept. of Electr. Eng., Soongsil Univ., Seoul, South Korea
Volume
1
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
992
Lastpage
996
Abstract
This paper presents block-based neural net works (BbNNs) for pattern classification. The BbNN achieves two goals: simultaneous optimization of network structure/weights; and implementation using reconfigurable digital hardware. The BbNN, in a 2-D array of basic blocks with four variable input/output nodes, successfully solve pattern classification problems. Internal structure is optimized using the GA with 2-D encoding scheme
Keywords
neural nets; optimisation; pattern recognition; 2D array; 2D encoding scheme; BbNN; GA; block-based neural networks; genetic algorithm; internal structure optimization; network structure; network weights; pattern classification; pattern recognition; reconfigurable digital hardware; simultaneous optimization; Artificial neural networks; Binary codes; Encoding; Evolutionary computation; Genetic algorithms; Neural network hardware; Neural networks; Pattern classification; Pattern recognition; Programmable logic arrays;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location
Honolulu, HI
ISSN
1098-7576
Print_ISBN
0-7803-7278-6
Type
conf
DOI
10.1109/IJCNN.2002.1005610
Filename
1005610
Link To Document