• 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