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Improved GWO and its application in parameter optimization of Elman neural network

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  • Wei Liu
  • Jiayang Sun
  • Guangwei Liu
  • Saiou Fu
  • Mengyuan Liu
  • Yixin Zhu
  • Qi Gao

Abstract

Traditional neural networks used gradient descent methods to train the network structure, which cannot handle complex optimization problems. We proposed an improved grey wolf optimizer (SGWO) to explore a better network structure. GWO was improved by using circle population initialization, information interaction mechanism and adaptive position update to enhance the search performance of the algorithm. SGWO was applied to optimize Elman network structure, and a new prediction method (SGWO-Elman) was proposed. The convergence of SGWO was analyzed by mathematical theory, and the optimization ability of SGWO and the prediction performance of SGWO-Elman were examined using comparative experiments. The results show: (1) the global convergence probability of SGWO was 1, and its process was a finite homogeneous Markov chain with an absorption state; (2) SGWO not only has better optimization performance when solving complex functions of different dimensions, but also when applied to Elman for parameter optimization, SGWO can significantly optimize the network structure and SGWO-Elman has accurate prediction performance.

Suggested Citation

  • Wei Liu & Jiayang Sun & Guangwei Liu & Saiou Fu & Mengyuan Liu & Yixin Zhu & Qi Gao, 2023. "Improved GWO and its application in parameter optimization of Elman neural network," PLOS ONE, Public Library of Science, vol. 18(7), pages 1-44, July.
    • Handle: RePEc:plo:pone00:0288071
    DOI: 10.1371/journal.pone.0288071
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    References listed on IDEAS

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    1. Youzhuang Sun & Junhua Zhang & Zhengjun Yu & Zhen Liu & Pengbo Yin, 2022. "WOA (Whale Optimization Algorithm) Optimizes Elman Neural Network Model to Predict Porosity Value in Well Logging Curve," Energies, MDPI, vol. 15(12), pages 1-14, June.
    2. Wei Liu & Zhiqing Guo & Feng Jiang & Guangwei Liu & Dong Wang & Zishun Ni, 2022. "Improved WOA and its application in feature selection," PLOS ONE, Public Library of Science, vol. 17(5), pages 1-33, May.
    3. Pan, Yongjun & Sun, Yu & Li, Zhixiong & Gardoni, Paolo, 2023. "Machine learning approaches to estimate suspension parameters for performance degradation assessment using accurate dynamic simulations," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Jicheng Liu & Yu Yin, 2022. "Power Load Forecasting Considering Climate Factors Based on IPSO-Elman Method in China," Energies, MDPI, vol. 15(3), pages 1-23, February.
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