Exponential H∞ synchronization and anti-synchronization of delayed discrete-time complex-valued neural networks with uncertainties

K. Sri Raja Priyanka, G. Soundararajan, Ardak Kashkynbayev and G. Nagamani

Mathematics and Computers in Simulation (MATCOM), 2023, vol. 207, issue C, 301-321

Abstract: This paper investigates the problem of exponential synchronization and anti-synchronization for uncertain discrete-time neural networks (NNs) having time-varying delays with H∞ performance in complex domain. An output-feedback controller is utilized not only to guarantee the synchronization criteria between the addressed discrete-time complex-valued neural networks (CVNNs) but also to reduce the effect of external disturbance. In order to assure the anti-synchronization criteria with H∞ performance for the proposed CVNNs, we have introduced the output-feedback controller by anti-synchronization error analysis. With the help of Lyapunov–Krasovskii functional (LKF), some linear matrix inequality (LMI) based sufficient conditions are derived for both synchronization and anti-synchronization criteria which can be validated through YALMIP toolbox in MATLAB software. At last, a numerical simulation result is provided to verify the correctness of the established theoretical results.

Keywords: Complex-valued neural networks; Synchronization; Anti-synchronization; Output-feedback control; Linear matrix inequality; Lyapunov–Krasovskii functional (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:207:y:2023:i:c:p:301-321

DOI: 10.1016/j.matcom.2023.01.002

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