DocumentCode
3466903
Title
Neural network combined with evolutionary algorithm for Knowledge Management in Electricity Supply Industry
Author
Cao, Xilin
Author_Institution
Xi´´an Railway Vocational & Tech. Inst., Xi´´an, China
Volume
2
fYear
2009
fDate
5-6 Dec. 2009
Firstpage
355
Lastpage
358
Abstract
A new method of designing BP neural networks based on evolutionary algorithm (EA) is proposed for knowledge management in electricity supply industry. The mechanisms of diversity maintaining and antibody density regulation exhibited in evolutionary system are introduced into evolutionary algorithm (EA). The proposed algorithm overcomes the problems of EA on search efficiency, individual diversity and premature and enhances the convergent performance effectively. In order to solve the problem of random initial weights, neuro fuzzy system for diversity is used to initialize weight vectors, and the detailed design steps of the algorithm are given. Simulated results show that the BP neural networks designed by EA have better performance in convergent speed and global convergence compared with hybrid evolutionary algorithm and the method is more accurate than other ones.
Keywords
backpropagation; evolutionary computation; fuzzy neural nets; knowledge management; power engineering computing; power markets; BP neural network; antibody density regulation; backpropagation; electricity supply industry; evolutionary algorithm; knowledge management; neuro fuzzy system; random initial weights; Algorithm design and analysis; Electricity supply industry; Evolutionary computation; Fuzzy systems; Knowledge management; Neural networks; Power system modeling; Power system planning; Power system reliability; Power system security; Evolutionary Algorithm; Knowledge Management; Neural Network;
fLanguage
English
Publisher
ieee
Conference_Titel
Test and Measurement, 2009. ICTM '09. International Conference on
Conference_Location
Hong Kong
Print_ISBN
978-1-4244-4699-5
Type
conf
DOI
10.1109/ICTM.2009.5413033
Filename
5413033
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