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The optimized GRNN based on the FDS-FOA under the hesitant fuzzy environment and its application in air quality index prediction

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Abstract

The generalized regression neural network (GRNN) is one of the most effective neural models in the field of information prediction. In this paper, we take advantage of the hesitant fuzzy set (HFS) and develop the GRNN under the hesitant fuzzy environment. However, the performance of the GRNN only depends on the smooth factor after determining the input and output variables. To determine the most appropriate smooth factor of network, we propose an improved fruit fly optimization algorithm with fast decreasing step (FDS-FOA) based on a dynamic step size function. A specific implementation process of the optimized GRNN based on FDS-FOA is also presented. Moreover, we apply the proposed model to the prediction of air quality index (AQI) in Beijing. Comparative analysis and sensitivity analysis are further conducted to illustrate the advantages of the optimized GRNN based on FDS-FOA.

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Authors and Affiliations

  1. Army Aviation Institute, Beijing, 101100, China

    Chenyang Song, Liguo Wang, Jian Hou & Yinhua Huang

  2. Command & Control Engineering College, Army Engineering University of PLA, Nanjing, 210001, China

    Chenyang Song

  3. Business School, Sichuan University, Chengdu, 610064, China

    Zeshui Xu

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  1. Chenyang Song
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  2. Liguo Wang
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  3. Jian Hou
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  4. Zeshui Xu
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  5. Yinhua Huang
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Correspondence to Zeshui Xu.

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Song, C., Wang, L., Hou, J. et al. The optimized GRNN based on the FDS-FOA under the hesitant fuzzy environment and its application in air quality index prediction. Appl Intell 51, 8365–8376 (2021). https://doi.org/10.1007/s10489-021-02350-1

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