@article{Huo2022, 
author = {Nina Huo and Bing Li and Yongkun Li},
title = {Global exponential stability and existence of almost periodic solutions in distribution for Clifford-valued stochastic high-order Hopfield neural networks with time-varying delays},
year = {2022},
journal = {AIMS Mathematics},
volume = {7},
number = {3},
pages = {3653-3679},
keywords = {Clifford-valued neural network, stochastic neural network, high-order Hopfield neural network, almost periodic solution in distribution, global exponential stability},
url = {https://www.sciopen.com/article/10.3934/math.2022202},
doi = {10.3934/math.2022202},
abstract = {In this paper, we consider a class of Clifford-valued stochastic high-order Hopfield neural networks with time-varying delays whose coefficients are Clifford numbers except the time delays. Based on the Banach fixed point theorem and inequality techniques, we obtain the existence and global exponential stability of almost periodic solutions in distribution of this class of neural networks. Even if the considered neural networks degenerate into real-valued, complex-valued and quaternion-valued ones, our results are new. Finally, we use a numerical example and its computer simulation to illustrate the validity and feasibility of our theoretical results.}
}