A new method to study global exponential stability of inertial neural networks with multiple time-varying transmission delays

Shuang Chang, Yantao Wang, Xian Zhang and Xin Wang

Mathematics and Computers in Simulation (MATCOM), 2023, vol. 211, issue C, 329-340

Abstract: In this article, the global exponential stability (GES) of inertial neural networks (INNs) are directly analyzed by proposing a new parameterized method. The parameterized representations of the states of neurons and their derivatives in the considered INNs are first given by introducing the relevant parameters. Furthermore, the sufficient conditions for the GES of the considered INNs are obtained by using the inequality technique. The obtained stability conditions consist of only a few simple linear scalar inequalities (LSIs) which are convenient to solve. Different from the previous works, the derived GES criteria do not involve any model transformation and any Lyapunov–Krasovskii functional (LKF), which reduces the computational complexity and simplifies the theoretical analysis. The last, a numerical simulation is presented to demonstrate the effectiveness of proposed parameterized method.

Keywords: Inertial neural networks; Global exponential stability; Parameterized method; Multiple time-varying transmission delays (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:211:y:2023:i:c:p:329-340

DOI: 10.1016/j.matcom.2023.04.008

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