IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v15y2019i2p1550147719832788.html
   My bibliography  Save this article

Parameter identification of a nonlinear model using an improved version of simulated annealing

Author

Listed:
  • Xiaoxia Tian
  • Jingwen Yan
  • Yanchun Yang
  • Chi Xiao
  • Qi Zhou

Abstract

This article aims to employ an improved simulated annealing algorithm to accurately and efficiently identify parameters of a nonlinear model which describes the nonlinear vortex-induced vertical force. In the general simulated annealing for vortex-induced vertical force models, the energy difference between the new and current solutions is very small so that the acceptance probability is close to 1. Almost all poorer solutions are accepted, which makes simulated annealing inefficient. To improve the performance of simulated annealing, an improved simulated annealing is proposed. First, the energy difference between the new and current solutions is amplified to put the acceptance probability in the interval of [0, 1]. Second, the length of the Markov chain is set as a function of the current temperature instead of the fixed value. Third, the generation criterion of the new solution is revised so that new solutions satisfy constraints and fully explore the neighborhood of the current solution. Simulation results show that improved simulated annealing has good performance in run-time and fitting. According to the results of Wilcoxon’s test, improved simulated annealing outperforms the other algorithms.

Suggested Citation

  • Xiaoxia Tian & Jingwen Yan & Yanchun Yang & Chi Xiao & Qi Zhou, 2019. "Parameter identification of a nonlinear model using an improved version of simulated annealing," International Journal of Distributed Sensor Networks, , vol. 15(2), pages 15501477198, February.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:2:p:1550147719832788
    DOI: 10.1177/1550147719832788
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1550147719832788
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1550147719832788?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
    ---><---

    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:sae:intdis:v:15:y:2019:i:2:p:1550147719832788. 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: SAGE Publications (email available below). General contact details of provider: .

    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.