@article{Dong2026, 
author = {Qijun Dong and Liguang Wan and Ailong Wu and Tao Li},
title = {Finite-time stability analysis of fractional-order Cohen–Grossberg neural networks with time-varying delays and fuzzy logics},
year = {2026},
journal = {AIMS Mathematics},
volume = {11},
number = {7},
pages = {23016-23049},
keywords = {fractional-order Cohen–Grossberg neural networks, fuzzy logics, finite-time stability, time-varying delays, Gronwall-type inequality},
url = {https://www.sciopen.com/article/10.3934/math.2026928},
doi = {10.3934/math.2026928},
abstract = {This paper addresses the finite-time stability problem for a class of fractional-order Cohen–Grossberg neural networks characterized by fuzzy logic operators and time-varying delays. The coexistence of variable delays and fuzzy feedback structures poses considerable challenges in deriving effective stability conditions. To tackle these difficulties, a delay-dependent estimation strategy based on a weighted envelope framework is developed to characterize the influence of delayed states. Subsequently, a novel fractional Gronwall-type inequality is established and utilized to derive a sufficient finite-time stability criterion for the investigated neural network model. The obtained criterion explicitly captures the effects of fuzzy min/max operations and time-varying delays. Finally, three simulation examples are provided to verify the applicability and effectiveness of the proposed theoretical results.}
}