IDEAS home Printed from https://ideas.repec.org/a/wly/jnlaaa/v2014y2014i1n178313.html
   My bibliography  Save this article

Radial Basis Function Neural Network Based on an Improved Exponential Decreasing Inertia Weight‐Particle Swarm Optimization Algorithm for AQI Prediction

Author

Listed:
  • Jinna Lu
  • Hongping Hu
  • Yanping Bai

Abstract

This paper proposed a novel radial basis function (RBF) neural network model optimized by exponential decreasing inertia weight particle swarm optimization (EDIW‐PSO). Based on the inertia weight decreasing strategy, we propose a new Exponential Decreasing Inertia Weight (EDIW) to improve the PSO algorithm. We use the modified EDIW‐PSO algorithm to determine the centers, widths, and connection weights of RBF neural network. To assess the performance of the proposed EDIW‐PSO‐RBF model, we choose the daily air quality index (AQI) of Xi’an for prediction and obtain improved results.

Suggested Citation

  • Jinna Lu & Hongping Hu & Yanping Bai, 2014. "Radial Basis Function Neural Network Based on an Improved Exponential Decreasing Inertia Weight‐Particle Swarm Optimization Algorithm for AQI Prediction," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnlaaa:v:2014:y:2014:i:1:n:178313
    DOI: 10.1155/2014/178313
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2014/178313
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2014/178313?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:wly:jnlaaa:v:2014:y:2014:i:1:n:178313. 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1155/4058 .

    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.