Open AccessGlobal exponential stability and existence of almost periodic solutions in distribution for Clifford-valued stochastic high-order Hopfield neural networks with time-varying delays
Author(s) -
Nina Huo,
AUTHOR_ID,
Bing Li,
Yongkun Li,
AUTHOR_ID,
AUTHOR_ID
Publication year - 2022
Publication title -
aims mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.329
H-Index - 15
ISSN - 2473-6988
DOI - 10.3934/math.2022202
Subject(s) - artificial neural network , class (philosophy) , mathematics , hopfield network , stability (learning theory) , order (exchange) , exponential stability , fixed point theorem , exponential function , distribution (mathematics) , degenerate energy levels , pure mathematics , computer science , mathematical analysis , nonlinear system , artificial intelligence , physics , finance , quantum mechanics , machine learning , economics
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.