Title :
Neural network combined with evolutionary algorithm for Knowledge Management in Electricity Supply Industry
Author_Institution :
Xi´´an Railway Vocational & Tech. Inst., Xi´´an, China
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;
Conference_Titel :
Test and Measurement, 2009. ICTM '09. International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-4699-5
DOI :
10.1109/ICTM.2009.5413033