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Neural Processing Letters

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09-08-2023

Global Results on Exponential Stability of Neutral Cohen–Grossberg Neural Networks Involving Multiple Neutral and Discrete Time-Varying Delays: A Method Based on System Solutions

Authors: Xian Zhang, Zhongjie Zhang, Tingting Yu, Xin Wang

Published in: Neural Processing Letters | Issue 8/2023

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Abstract

In this paper, we study the global exponential stability of a class of neutral-type Cohen–Grossberg neural networks (NTCGNNs) with multiple discrete and neutral time varying delays. Since the network model cannot be represented in the form of vector–matrix, a method based on system solutions is proposed to establish novel global exponential stability criteria for the NTCGNNs under consideration. This method can results in global exponential stability criteria consists of only a few linear scalar inequalities, and it does not need to set up any Lyapunov–Krasovskii functionals, which can greatly reduce a large amount of computations. Compared with the existing ones, the applicability of the obtained stability conditions are explained by two representative numerical examples. It is worth emphasizing that the method ground upon system solutions is first proposed, and is applicable for system models which can or cannot be represented in the form of vector–matrix.

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Title: Global Results on Exponential Stability of Neutral Cohen–Grossberg Neural Networks Involving Multiple Neutral and Discrete Time-Varying Delays: A Method Based on System Solutions
Authors: Xian Zhang
Zhongjie Zhang
Tingting Yu
Xin Wang
Publication date: 09-08-2023
Publisher: Springer US
Published in: Neural Processing Letters / Issue 8/2023
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-023-11375-1

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