Non-reduced order method to global h-stability criteria for proportional delay high-order inertial neural networks

Junlan Wang, Xin Wang, Yantao Wang and Xian Zhang

Applied Mathematics and Computation, 2021, vol. 407, issue C

Abstract: This article mainly explores the global h-stability for proportional delay high-order inertial neural networks. Without adopting reduced order method, a new Lyapunov–Krasovskii functional is constructed to derive the delay-dependent global h-stability criterion, which is new and improves some previous works. Moreover, the approach proposed in this article is also applicable to the global h-stability for multiple proportional delay high-order inertial neural networks. Finally, three examples and their numerical simulations are presented to illustrate the effectiveness of the method.

Keywords: High-order inertial neural networks; Proportional delays; Global h-stability; Non-reduced order method (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:407:y:2021:i:c:s0096300321003970

DOI: 10.1016/j.amc.2021.126308

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