• DocumentCode
    522844
  • Title

    Research on Sensitivity Analysis of Operational Effectiveness Based on LS-SVM

  • Author

    Li, Guo-Lin ; Dong Li ; Qu, Zhao-Hui ; Qin, Miao

  • Author_Institution
    No.7 Dept., Naval Aeronaut. & Astronaut. Univ., Yantai, China
  • Volume
    2
  • fYear
    2010
  • fDate
    4-6 June 2010
  • Firstpage
    305
  • Lastpage
    309
  • Abstract
    In efficiency analysis of weapon system, in order to capture and represent the decision maker´s preferences and then to select the most desirable alternative, sensitivity analysis method of operational effectiveness based on LS-SVM is proposed. Firstly, the principle of effectiveness evaluation method based on LS-SVM is discussed. Secondly, to extract learning samples from the MADM problem, an approach to estimate the utility functions for attributes is presented. An optimal selection approach of LS-SVM parameters was put forward based on particle swarm optimization (PSO) algorithm. Finally, the implementation algorithm of sensitivity analysis method of operational effectiveness based on LS-SVM is presented. The proposed method has less number of parameter and is simple and reliable, comparing with SVM. In the end, an example demonstrates the method is feasible and availability.
  • Keywords
    least squares approximations; military computing; particle swarm optimisation; sensitivity analysis; support vector machines; weapons; LS SVM; MADM problem; operational effectiveness; particle swarm optimization; sensitivity analysis; weapon system; Arithmetic; Decision making; Difference equations; Differential equations; Learning systems; Least squares methods; Machine learning; Sensitivity analysis; Support vector machines; Weapons; effectiveness evaluation; least square support vector machine (LS-SVM); sensitivity analysis; weapon equipment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information and Computing (ICIC), 2010 Third International Conference on
  • Conference_Location
    Wuxi, Jiang Su
  • Print_ISBN
    978-1-4244-7081-5
  • Electronic_ISBN
    978-1-4244-7082-2
  • Type

    conf

  • DOI
    10.1109/ICIC.2010.172
  • Filename
    5513845