Finite-time stabilization of discontinuous fuzzy inertial Cohen–Grossberg neural networks with mixed time-varying delays
Articles
Fanchao Kong
Anhui Normal University
Quanxin Zhu
Hunan Normal University
https://orcid.org/0000-0003-3130-4923
Rathinasamy Sakthivel
Bharathiar University
https://orcid.org/0000-0002-5528-2709
Published 2021-09-01
https://doi.org/10.15388/namc.2021.26.23935
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Keywords

inertial Cohen–Grossberg neural networks,
uzzy logics
discrete and distributed timevarying delays
Lyapunov–Krasovskii functional
finite-time stabilization

How to Cite

Kong F., Zhu Q. and Sakthivel R. (2021) “Finite-time stabilization of discontinuous fuzzy inertial Cohen–Grossberg neural networks with mixed time-varying delays”, Nonlinear Analysis: Modelling and Control, 26(5), pp. 759-780. doi: 10.15388/namc.2021.26.23935.

Abstract

This article aims to study a class of discontinuous fuzzy inertial Cohen–Grossberg neural networks (DFICGNNs) with discrete and distributed time-delays. First of all, in order to deal with the discontinuities by the differential inclusion theory, based on a generalized variable transformation including two tunable variables, the mixed time-varying delayed DFICGNN is transformed into a first-order differential system. Then, by constructing a modified Lyapunov–Krasovskii functional concerning with the mixed time-varying delays and designing a delayed feedback control law, delay-dependent criteria formulated by algebraic inequalities are derived for guaranteeing the finite-time stabilization (FTS) for the addressed system. Moreover, the settling time is estimated. Some related stability results on inertial neural networks is extended. Finally, two numerical examples are carried out to verify the effectiveness of the established results.

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