DocumentCode :
302641
Title :
Contour refinement by enhanced query-based learning
Author :
Huang, Shyh-Jier ; Hung, Chuan-Chang
Author_Institution :
Dept. of Electr. Eng., Kaohsiung Polytech. Inst., Taiwan
Volume :
2
fYear :
1996
fDate :
12-15 May 1996
Firstpage :
616
Abstract :
An enhanced query-based learning neural network is proposed to refine the contour in this paper. The proposed learning approach provides a classifier at a lower computational cost comparing with the other method. The ambiguous regions in the classification process can be thus easier clarified. The proposed approach has been tested on the refinement of security contours in the assessment of power system operations. Results demonstrate the effectiveness and feasibility of the proposed approach for the applications
Keywords :
learning (artificial intelligence); neural nets; pattern classification; power system security; computational cost; contour refinement; pattern classification; power system security; query-based learning neural network; Computational efficiency; Computer networks; Concurrent computing; Genetic algorithms; Learning systems; Neural networks; Parallel processing; Power system security; System testing; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuits and Systems, 1996. ISCAS '96., Connecting the World., 1996 IEEE International Symposium on
Conference_Location :
Atlanta, GA
Print_ISBN :
0-7803-3073-0
Type :
conf
DOI :
10.1109/ISCAS.1996.541800
Filename :
541800
Link To Document :
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