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Parameter estimation for fractional-order nonlinear systems based on improved sparrow search algorithm

Author

Listed:
  • Yongqiang Zhou

    (Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, P. R. China†Department of Physics and Electronic Engineering, Guangxi Normal University for Nationalities, Chongzuo 532200, P. R. China)

  • Renhuan Yang

    (��College of Information Science and Technology, Jinan University, Guangzhou 510632, P. R. China)

  • Yibin Chen

    (��College of Information Science and Technology, Jinan University, Guangzhou 510632, P. R. China)

  • Qidong Huang

    (��College of Information Science and Technology, Jinan University, Guangzhou 510632, P. R. China)

  • Chao Shen

    (��College of Information Science and Technology, Jinan University, Guangzhou 510632, P. R. China)

  • Xiuzeng Yang

    (��Department of Physics and Electronic Engineering, Guangxi Normal University for Nationalities, Chongzuo 532200, P. R. China)

  • Ling Zhang

    (�Experiment and Training Center, Guangzhou Vocational College of Technology & Business, Guangzhou 510632, P. R. China)

  • Mengyu Wei

    (�Faculty of Science and Technology, University of Macau, Macau 999078, P. R. China)

Abstract

Parameter estimation is important in the study of control and synchronization of fractional-order nonlinear systems (FONSs). This paper proposes an improved Sparrow Search Algorithm (ISSA) for the parameter estimation problem of FONSs. The algorithm improves the population initialization, position update method of discoverers and warning sparrows based on Sparrow Search Algorithm (SSA), and the parameter estimation simulation experiment for fractional-order financial nonlinear system and fractional-order L nonlinear system is conducted to demonstrate this method. The experimental results show that the proposed ISSA is superior to the SSA, Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA) and Harris Hawks Optimization (HHO) in terms of parameter optimization accuracy and convergence speed, which validates the advantages of the ISSA.

Suggested Citation

  • Yongqiang Zhou & Renhuan Yang & Yibin Chen & Qidong Huang & Chao Shen & Xiuzeng Yang & Ling Zhang & Mengyu Wei, 2024. "Parameter estimation for fractional-order nonlinear systems based on improved sparrow search algorithm," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 35(10), pages 1-14, October.
    • Handle: RePEc:wsi:ijmpcx:v:35:y:2024:i:10:n:s0129183124501316
    DOI: 10.1142/S0129183124501316
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