IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/5810174.html
   My bibliography  Save this article

Application of Elman Neural Network Based on Genetic Algorithm in Initial Alignment of SINS for Guided Projectile

Author

Listed:
  • Lei Sun
  • Wenjun Yi
  • Dandan Yuan
  • Jun Guan

Abstract

The purpose of this paper is to present an in-flight initial alignment method for the guided projectiles, obtained after launching, and utilizing the characteristic of the inertial device of a strapdown inertial navigation system. This method uses an Elman neural network algorithm, optimized by genetic algorithm in the initial alignment calculation. The algorithm is discussed in details and applied to the initial alignment process of the proposed guided projectile. Simulation results show the advantages of the optimized Elman neural network algorithm for the initial alignment problem of the strapdown inertial navigation system. It can not only obtain the same high-precision alignment as the traditional Kalman filter but also improve the real-time performance of the system.

Suggested Citation

  • Lei Sun & Wenjun Yi & Dandan Yuan & Jun Guan, 2019. "Application of Elman Neural Network Based on Genetic Algorithm in Initial Alignment of SINS for Guided Projectile," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-9, April.
    • Handle: RePEc:hin:jnlmpe:5810174
    DOI: 10.1155/2019/5810174
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2019/5810174.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2019/5810174.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2019/5810174?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tayo Uthman Badrudeen & Nnamdi I. Nwulu & Saheed Lekan Gbadamosi, 2023. "Neural Network Based Approach for Steady-State Stability Assessment of Power Systems," Sustainability, MDPI, vol. 15(2), pages 1-13, January.

    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:5810174. 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.