Skip to main content
placeholder image

Parameter Identification for Memristive Chaotic System Using Modified Sparrow Search Algorithm

Journal Article


Abstract


  • A memristor is a non-linear element. The chaotic system constructed by it can improve its unpredictability and complexity. Parameter identification of a memristive chaotic system is the primary task to implement chaos control and synchronization. To identify the unknown parameters accurately and quickly, we introduce the Sine Pareto Sparrow Search Algorithm (SPSSA), a modified sparrow search algorithm (SSA). in this research. Firstly, we introduce the Pareto distribution to alter the scroungers’ location in the SSA. Secondly, we use a sine-cosine strategy to improve the producers’ position update. These measures can effectively accelerate the convergence speed and avoid local optimization. Thirdly, the SPSSA is used to identify the parameters of a memristive chaotic system. The proposed SPSSA exceeds the classic SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC) in simulations based on the five benchmark functions. The simulation results of parameter identification of a memristive chaotic system show that the method is feasible, and the algorithm has a fast convergence speed and high estimation accuracy.

Publication Date


  • 2022

Citation


  • Xiong, Q., Shen, J., Tong, B., & Xiong, Y. (2022). Parameter Identification for Memristive Chaotic System Using Modified Sparrow Search Algorithm. Frontiers in Physics, 10. doi:10.3389/fphy.2022.912606

Scopus Eid


  • 2-s2.0-85133917584

Volume


  • 10

Abstract


  • A memristor is a non-linear element. The chaotic system constructed by it can improve its unpredictability and complexity. Parameter identification of a memristive chaotic system is the primary task to implement chaos control and synchronization. To identify the unknown parameters accurately and quickly, we introduce the Sine Pareto Sparrow Search Algorithm (SPSSA), a modified sparrow search algorithm (SSA). in this research. Firstly, we introduce the Pareto distribution to alter the scroungers’ location in the SSA. Secondly, we use a sine-cosine strategy to improve the producers’ position update. These measures can effectively accelerate the convergence speed and avoid local optimization. Thirdly, the SPSSA is used to identify the parameters of a memristive chaotic system. The proposed SPSSA exceeds the classic SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC) in simulations based on the five benchmark functions. The simulation results of parameter identification of a memristive chaotic system show that the method is feasible, and the algorithm has a fast convergence speed and high estimation accuracy.

Publication Date


  • 2022

Citation


  • Xiong, Q., Shen, J., Tong, B., & Xiong, Y. (2022). Parameter Identification for Memristive Chaotic System Using Modified Sparrow Search Algorithm. Frontiers in Physics, 10. doi:10.3389/fphy.2022.912606

Scopus Eid


  • 2-s2.0-85133917584

Volume


  • 10