• DocumentCode
    1589554
  • Title

    An Improved LS-SVM Based on Quantum PSO Algorithm and Its Application

  • Author

    Pan, Guofeng ; Xia, Kewen ; Dong, Yao ; Shi, Jin

  • Author_Institution
    Hebei Univ. of Technol., Tianjin
  • Volume
    2
  • fYear
    2007
  • Firstpage
    606
  • Lastpage
    610
  • Abstract
    In order to avoid the problem of inverse matrix calculation in LS-SVM algorithm, an improved LS-SVM based on quantum PSO algorithm is presented, the main process is to encode the particle swarm with quantum bit, then solve the linear equation set with the iterative quantum PSO algorithm. So the training velocity of LS- SVM algorithm is improved, the computer memory is saved, and the least square solution is always obtained. The actual application in Changqing oil-field indicates the application effect is better than that of classical SVM and LM neural network in oil layer recognition, the improved LS-SVM algorithm not only improves the accuracy of recognition, but also accelerates the velocity of convergence, and the result of oil layer recognition is fully accord with that of oil trial.
  • Keywords
    iterative methods; least squares approximations; matrix algebra; neural nets; particle swarm optimisation; petroleum industry; quantum computing; support vector machines; LM neural network; LS-SVM; computer memory; inverse matrix calculation; iterative quantum PSO algorithm; least square solution; linear equation set; oil layer recognition; particle swarm; quantum PSO algorithm; Acceleration; Application software; Equations; Iterative algorithms; Least squares methods; Particle swarm optimization; Petroleum; Quantum computing; Support vector machine classification; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation, 2007. ICNC 2007. Third International Conference on
  • Conference_Location
    Haikou
  • Print_ISBN
    978-0-7695-2875-5
  • Type

    conf

  • DOI
    10.1109/ICNC.2007.218
  • Filename
    4344422