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
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