Global robust stability of fuzzy cellular neural networks with parameter uncertainties

Tiecheng Zhang; Wei He; Tiecheng Zhang; Wei He

doi:10.3934/math.2024392

AIMS Mathematics

2024, Volume 9, Issue 4: 8063-8078. doi: 10.3934/math.2024392

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Global robust stability of fuzzy cellular neural networks with parameter uncertainties

Tiecheng Zhang ^1,2,
Wei He ^{3
,
,}

1.
School of Mathematics and Statistics, Hubei Normal University, Huangshi, 435002, China
2.
Huangshi Key Laboratory of Metaverse and Virtual Simulation, Hubei Normal University, Huangshi, 435002, China
3.
School of Mathematics and Statistics, Hubei University of Science and Technology, Xianning, 437000, China

Received: 28 December 2023 Revised: 03 February 2024 Accepted: 08 February 2024 Published: 26 February 2024
MSC : 93D09, 93D20, 93D23

The global robust stability of uncertain delayed fuzzy cellular neural networks (UDFCNNs) was analyzed in this paper. The major results of this paper provided some new criteria for the existence and uniqueness of the equilibrium point of UDFCNN. Furthermore, suitable Lyapunov-Krasovskii functionals was designed for obtaining the adequate conditions for the global asymptotic robust stability and global exponential robust stability of UDFCNN. Finally, several numerical examples was provided to verify the validity of the results.
- fuzzy cellular neural network,
- time delay,
- parameter uncertainties,
- global asymptotic robust stability,
- global exponential robust stability
Citation: Tiecheng Zhang, Wei He. Global robust stability of fuzzy cellular neural networks with parameter uncertainties[J]. AIMS Mathematics, 2024, 9(4): 8063-8078. doi: 10.3934/math.2024392

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Abstract

The global robust stability of uncertain delayed fuzzy cellular neural networks (UDFCNNs) was analyzed in this paper. The major results of this paper provided some new criteria for the existence and uniqueness of the equilibrium point of UDFCNN. Furthermore, suitable Lyapunov-Krasovskii functionals was designed for obtaining the adequate conditions for the global asymptotic robust stability and global exponential robust stability of UDFCNN. Finally, several numerical examples was provided to verify the validity of the results.

References

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