Robustness analysis of fuzzy BAM cellular neural network with time-varying delays and stochastic disturbances

Wenxiang Fang; Tao Xie; Biwen Li; Wenxiang Fang; Tao Xie; Biwen Li

doi:10.3934/math.2023471

AIMS Mathematics

2023, Volume 8, Issue 4: 9365-9384. doi: 10.3934/math.2023471

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Research article

Robustness analysis of fuzzy BAM cellular neural network with time-varying delays and stochastic disturbances

School of mathematics and statistics, Hubei Normal University, Huangshi 435002, Hubei, China

Received: 10 December 2022 Revised: 29 January 2023 Accepted: 06 February 2023 Published: 15 February 2023
MSC : 93B35, 93D23

Robustness analysis for the global exponential stability of fuzzy bidirectional associative memory cellular neural network (FBAMCNN) is explored in this paper. By applying Gronwall-Bellman lemma and other inequality techniques, the range limits of both time-varying delays and the intensity of noise that FBAMCNN can withstand to maintain globally exponentially stable is estimated. It means that if the intensities of interference are larger than the bounds we derived, then the perturbed system may lose global exponential stability. Several instances are given to support our main results.
- robustness analysis,
- fuzzy bidirectional memory cellular neural network,
- time-varying delays,
- stochastic disturbances
Citation: Wenxiang Fang, Tao Xie, Biwen Li. Robustness analysis of fuzzy BAM cellular neural network with time-varying delays and stochastic disturbances[J]. AIMS Mathematics, 2023, 8(4): 9365-9384. doi: 10.3934/math.2023471

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Abstract

Robustness analysis for the global exponential stability of fuzzy bidirectional associative memory cellular neural network (FBAMCNN) is explored in this paper. By applying Gronwall-Bellman lemma and other inequality techniques, the range limits of both time-varying delays and the intensity of noise that FBAMCNN can withstand to maintain globally exponentially stable is estimated. It means that if the intensities of interference are larger than the bounds we derived, then the perturbed system may lose global exponential stability. Several instances are given to support our main results.

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