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

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  • 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, Hindawi, vol. 2014, pages 1-9, July.
    • Handle: RePEc:hin:jnlaaa:178313
    DOI: 10.1155/2014/178313
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    Cited by:

    1. Le Thi Nhu Ngoc & Minjeong Kim & Vu Khac Hoang Bui & Duckshin Park & Young-Chul Lee, 2018. "Particulate Matter Exposure of Passengers at Bus Stations: A Review," IJERPH, MDPI, vol. 15(12), pages 1-20, December.
    2. Ying Wang & Jianzhou Wang & Hongmin Li & Hufang Yang & Zhiwu Li, 2022. "Multi‐step air quality index forecasting via data preprocessing, sequence reconstruction, and improved multi‐objective optimization algorithm," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1483-1511, November.

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