@article{Zhang2026, 
author = {Tiecheng Zhang and Wenxiang Fang and Liyan Wang and Yulong Lu},
title = {Finite-time stability of fractional-order fuzzy inertial neural networks via a new finite-time inequality},
year = {2026},
journal = {AIMS Mathematics},
volume = {11},
number = {3},
pages = {5936-5953},
keywords = {inertial neural network, finite-time stability, fractional order, fuzzy logics},
url = {https://www.sciopen.com/article/10.3934/math.2026245},
doi = {10.3934/math.2026245},
abstract = {This paper investigates the finite-time stability (FTS) of a class of fractional-order fuzzy inertial neural networks (FOFINNs) with time-varying delays. The existing finite-time inequalities for fractional-order systems pose significant theoretical and practical limitations, as they can yield unbounded control inputs when the system state approaches zero. To address this issue, this work introduces a novel finite-time inequality. By incorporating a positive constant into the inequality structure, the proposed method effectively bounds the control signal, ensuring its practical realizability. Utilizing this inequality within a Lyapunov framework alongside an order reduction method (ORM), a composite feedback controller is designed to achieve FTS. Sufficient stability conditions are derived, and an explicit, computable upper bound for the settling time is established. Numerical simulations validate the theoretical results and demonstrate the method's superiority over existing approaches.}
}