@article{Zhang2024, 
author = {Tiecheng Zhang and Liyan Wang and Yuan Zhang and Jiangtao Deng},
title = {Finite-time stability for fractional-order fuzzy neural network with mixed delays and inertial terms},
year = {2024},
journal = {AIMS Mathematics},
volume = {9},
number = {7},
pages = {19176-19194},
keywords = {finite-time stability, fractional-order, mixed delays, fuzzy, inertial neural network},
url = {https://www.sciopen.com/article/10.3934/math.2024935},
doi = {10.3934/math.2024935},
abstract = {This paper explored the finite-time stability (FTS) of fractional-order fuzzy inertial neural network with mixed delays. First, the dimension of the model was reduced by the order reduction method. Second, by leveraging the fractional-order finite-time stability theorem, fractional calculus and inequality methods, we established some sufficient conditions to guarantee the FTS of the model under feasible delay-dependent feedback controller and delay-dependent adaptive controller, respectively. Additionally, we derived the settling times (STs) for each control strategy. Finally, we provided two examples to substantiate our findings.}
}