Artificial neural network (ANN) is the backbone of machine learning, specifically deep
learning. The interpolating and learning ability of an ANN makes it an ideal tool for …

It is well known that the conventional fractional-order neural networks (FONNs) cannot
generate nonconstant periodic oscillation. For this point, this article discusses a class of …

A class of semilinear fractional difference equations is introduced in this paper. The fixed
point theorem is adopted to find stability conditions for fractional difference equations. The …

This article discusses the global Mittag-Leffler projective synchronization (GM-LPS) on
fractional-order quaternion-valued neural networks (FOQVNNs) including mixed delays and …

In recent years, there have been a lot of studies focusing on the dynamics of fractional-order
neural networks (FONNs). One problem is that the standard Lyapunov theory does not apply …

This paper is concerned with finite-time synchronization and finite-time synchronization for
coupled neural networks with multiple state/derivative couplings. Firstly, several sufficient …

Finite-time synchronization (FTS) of uncertain fractional-order memristive neural networks
(FMNNs) with leakage and discrete delays is studied in this paper, in which the impacts of …

This study principally deals with the stability property and the emergence of Hopf bifurcation
for fractional-order genetic regulatory networks incorporating distributed delays and discrete …

As multi-gene networks transmit signals and products by synchronous cooperation,
investigating the synchronization of gene regulatory networks may help us to explore the …

This paper is dedicated to the global Mittag-Leffler synchronization (GMLS) of fractional-
order inertial-type neural networks (FOITNNs) with time delays. To begin with, based on the …