Fixed-time synchronization of fractional-order Hopfield neural networks with proportional delays

Pushpendra Kumar and El Abed Assali

Mathematics and Computers in Simulation (MATCOM), 2026, vol. 240, issue C, 367-380

Abstract: This article explores the fixed-time synchronization of fractional-order Hopfield neural networks incorporating proportional delays. Unlike finite-time synchronization, where the convergence time varies based on the initial synchronization errors, fixed-time synchronization allows for a predetermined settling time that remains independent of initial conditions. To achieve fixed-time synchronization, two types of feedback control strategies incorporating fractional integrals are employed: one based on state feedback and another utilizing a controller designed with a Lyapunov function and an exponential function. By designing appropriate Lyapunov functions and employing inequality techniques, multiple sufficient conditions were established to guarantee the fixed-time synchronization of the considered systems under these control strategies. Finally, two numerical examples are presented to demonstrate the validity and practical relevance of the theoretical findings.

Keywords: Fractional-order; Hopfield neural networks; Fixed-time synchronization; Control (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:240:y:2026:i:c:p:367-380

DOI: 10.1016/j.matcom.2025.07.035

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