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

Yongqiang Zhou, Renhuan Yang, Yibin Chen, Qidong Huang, Chao Shen, Xiuzeng Yang, Ling Zhang and Mengyu Wei
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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

International Journal of Modern Physics C (IJMPC), 2024, vol. 35, issue 10, 1-14

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.

Keywords: Fractional-order nonlinear systems; parameter estimation; improved sparrow search algorithm; swarm intelligent optimization algorithm (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0129183124501316

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