Impulsive synchronization and parameter optimization for disturbed inertial memristive neural networks with actuator saturation

Haibin He, Nan Jiang, Xiaoyang Liu, Lu Fan and Jinde Cao

Physica A: Statistical Mechanics and its Applications, 2025, vol. 674, issue C

Abstract: This paper investigates the finite-time impulsive synchronization of inertial memristive neural networks (IMNNs) with actuator saturation, parameter uncertainties, and external disturbances. A polytopic representation approach is utilized to handle input saturation and a robust impulsive controller is designed to guarantee finite-time synchronization and some criteria are derived. Additionally, an improved adaptive weighted particle swarm optimization (AWPSO) approach is introduced to obtain energy-saving control parameters. This novel approach reduces the control cost and the settling time for the synchronization of IMNNs compared to traditional methods like standard particle swarm optimization (PSO). One practical example is presented to illustrate the validity of the proposed algorithms.

Keywords: Finite-time impulsive synchronization; Inertial memristive neural networks (IMNNs); Actuator saturation; Optimal control parameters (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:674:y:2025:i:c:s0378437125004078

DOI: 10.1016/j.physa.2025.130755

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