IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/587925.html
   My bibliography  Save this article

Intuitionistic Fuzzy Kernel Matching Pursuit Based on Particle Swarm Optimization for Target Recognition

Author

Listed:
  • Xiaodong Yu
  • Yingjie Lei
  • Shaohua Yue
  • Feixiang Meng

Abstract

In order to overcome the long training time caused by searching optimal basic functions based on greedy strategy from a redundant basis function dictionary for the intuitionistic fuzzy kernel matching pursuit (IFKMP), the particle swarm optimization algorithm with powerful ability of global search and quick convergence rate is applied to speed up searching optimal basic function data in function dictionary. The approach of intuitionistic fuzzy kernel matching pursuit based on particle swarm optimization algorithm, namely, PS-IFKMP, is proposed. This algorithm is applied to the aerospace target recognition, which requires real-time ability. Simulation results show that, compared with the conventional approaches, the proposed algorithm can decrease training time and improve calculation efficiency obviously with almost unchanged classification accuracy, while the model has better sparsity and generalization. It is also demonstrated that this approach is suitable for the application requiring both accuracy and efficiency.

Suggested Citation

  • Xiaodong Yu & Yingjie Lei & Shaohua Yue & Feixiang Meng, 2015. "Intuitionistic Fuzzy Kernel Matching Pursuit Based on Particle Swarm Optimization for Target Recognition," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-11, June.
    • Handle: RePEc:hin:jnlmpe:587925
    DOI: 10.1155/2015/587925
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2015/587925.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2015/587925.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2015/587925?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:587925. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.