A new approach for accuracy-preassigned finite-time exponential synchronization of neutral-type Cohen–Grossberg memristive neural networks involving multiple time-varying leakage delays

We studied the problem of accuracy-preassigned finite-time exponential synchronization of neutral-type Cohen–Grossberg memristive neural networks involving time-varying multiple leakage and transmission delays. First, a novel method was presented to give an estimation formula for solutions of the error system. Then, the estimation formula was used to establish sufficient conditions guaranteeing accuracy-preassigned finite-time exponential synchronization of the considered memristive Cohen–Grossberg neural networks. The obtained sufficient conditions were composed of some linear scalar inequalities that was easy to solve by employing standard tool softwares. Moreover, the approach proposed here was based on the concept of accuracy-preassigned finite-time exponential synchronization, and Lyapunov–Krasovskii functionals or model transformations were not involved, simplifying the theorematic proof. Finally, two numerical examples were given to present the validity of theorematic results.

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Publication Details

Journal
AIMS Mathematics
Published
2025-01-01
DOI
https://doi.org/10.3934/math.2025496
Primary Topic
Neural Networks Stability and Synchronization
Type
article
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article

A new approach for accuracy-preassigned finite-time exponential synchronization of neutral-type Cohen–Grossberg memristive neural networks involving multiple time-varying leakage delays

Yantao Wang, Er-yong Cong, Li Zhu, Xian Zhang
AIMS Mathematics
Neural Networks Stability and Synchronization
article

A new approach for accuracy-preassigned finite-time exponential synchronization of neutral-type Cohen–Grossberg memristive neural networks involving multiple time-varying leakage delays

Yantao Wang, Er-yong Cong, Li Zhu, Xian Zhang
article en

Abstract

We studied the problem of accuracy-preassigned finite-time exponential synchronization of neutral-type Cohen–Grossberg memristive neural networks involving time-varying multiple leakage and transmission delays. First, a novel method was presented to give an estimation formula for solutions of the error system. Then, the estimation formula was used to establish sufficient conditions guaranteeing accuracy-preassigned finite-time exponential synchronization of the considered memristive Cohen–Grossberg neural networks. The obtained sufficient conditions were composed of some linear scalar inequalities that was easy to solve by employing standard tool softwares. Moreover, the approach proposed here was based on the concept of accuracy-preassigned finite-time exponential synchronization, and Lyapunov–Krasovskii functionals or model transformations were not involved, simplifying the theorematic proof. Finally, two numerical examples were given to present the validity of theorematic results.

AIMS MathematicsVol. 10(5)
Harbin University (CN), Heilongjiang University (CN)
Openalex Percentile: Top 89%
Neural Networks Stability and Synchronization
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A new approach for accuracy-preassigned finite-time exponential synchronization of neutral-type Cohen–Grossberg memristive neural networks involving multiple time-varying leakage delays — Yantao Wang, Er-yong Cong, et al. · AIMS Mathematics (2025) | TGRS Research Map | TGRS