Exponential synchronization of memristive neural networks with inertial and nonlinear coupling terms: Pinning impulsive control approaches

Qianhua Fu, Shouming Zhong and Kaibo Shi

Applied Mathematics and Computation, 2021, vol. 402, issue C

Abstract: This paper investigates exponential synchronization for memristive neural networks (MNNs) with inertial and nonlinear coupling terms. Two novel hybrid mode-dependent pinning impulse control approaches are proposed, one is adaptive element-selection and pinning a part of elements in each identical node, and the other is fixed node-selection and pinning a part of identical nodes. By introducing appropriate variable substitution, the initial second-order state derivative model for MNNs is transformed into two first-order derivative equations. Then, through introducing average impulsive interval, Lyapunov–Krasovskii functional method, inequality techniques, and extended comparison principle, some corresponding exponential synchronization conditions are presented, which enrich and extend some published results. Finally, simulations are given to illustrate the exponential synchronization conditions.

Keywords: Nonlinear coupling; Inertial; Memristor; Synchronization; Hybrid pinning impulsive (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:402:y:2021:i:c:s0096300321002599

DOI: 10.1016/j.amc.2021.126169

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