Open AccessOn fractional variable-order neural networks with time-varying external inputs
Author(s) -
Amel Hioual,
Adel Ouannas
Publication year - 2022
Language(s) - English
DOI - 10.55059/ijm.2022.1.1/5
Subject(s) - uniqueness , equilibrium point , mathematics , exponential stability , control theory (sociology) , synchronization (alternating current) , variable (mathematics) , artificial neural network , lyapunov stability , stability (learning theory) , class (philosophy) , chaotic , controller (irrigation) , stability theory , computer science , mathematical analysis , differential equation , nonlinear system , topology (electrical circuits) , control (management) , physics , quantum mechanics , artificial intelligence , combinatorics , machine learning , agronomy , biology
This research discuss the existence, uniqueness, asymptotic stability, and global asymptotic synchronization of a class of Caputo variable-order neural networks with time-varying external inputs. Theory of contraction mapping is used to establish a sufficient condition for determining the existence and uniqueness of the equilibrium point. Using the variable fractional Lyapunov approach, we investigate the asymptotic stability of the unique equilibrium. Synchronization of variable-order chaotic networks is also studied using an effective controller. Three numerical examples are provided to show the efficacy of the results obtained.