A matrix-separation-based integral inequality for aperiodic sampled-data synchronization of delayed neural networks considering communication delay

H.-Z. Wang, X.-C. Shangguan, D. Xiong, Y.-H. An and L. Jin

Applied Mathematics and Computation, 2025, vol. 486, issue C

Abstract: This paper achieves the synchronization of delayed neural networks (DNNs) considering aperiodic sampled-data control and communication delay. First of all, based on the master-slave DNNs with aperiodic sampling synchronization controller, a synchronization error system is constructed. Then, an augmented functional containing both the error state and its derivative is constructed. Compared with the existing researches, the augmented functional introduces more cross information of error states to the criterion. Next, an integral inequality based on the separation of internal integral variable and matrix is developed. Compared to the inequalities that treat the internal variable and the matrix as unified ones, the developed inequality provides a tighter estimate of the derivative of the augmented functional. On this basis, a criterion with less conservative is developed for the aperiodic sampled-data synchronization of DNNs considering communication delay. Finally, to indicate the superiority of the developed method on improving the acceptable sampling upper bound of synchronization, three numerical examples are provided.

Keywords: Delayed neural networks; Master-slave synchronization; Aperiodic sampled-data control; Matrix-separation-based integral inequality (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:486:y:2025:i:c:s0096300324004934

DOI: 10.1016/j.amc.2024.129032

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