In this work, a bibliographic analysis on artificial neural networks (ANNs) using fractional
calculus (FC) theory has been developed to summarize the main features and applications …

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 …

The present paper introduces memristor-based fractional-order neural networks. The
conditions on the global Mittag-Leffler stability and synchronization are established by using …

This paper addresses the global stabilization of fractional-order memristor-based neural
networks (FMNNs) with time delay. The voltage threshold type memristor model is …

Discrete fractional calculus is suggested to describe neural networks with memory effects.
Fractional discrete-time recurrent neural network is proposed on an isolated time scale …

This paper is concerned with the adaptive synchronization problem of fractional-order
memristor-based neural networks with time delay. By combining the adaptive control, linear …

This paper investigates the projective synchronization of fractional-order memristor-based
neural networks. Sufficient conditions are derived in the sense of Caputo's fractional …

In this paper, the problems of stability and Hopf bifurcation in a class of fractional-order
complex-valued single neuron model with time delay are addressed. With the help of the …

This paper deals with the problem of synchronization of fractional-order complex-valued
neural networks with time delays. By means of linear delay feedback control and a fractional …

In this paper, the global stability analysis of fractional-order Hopfield neural networks with
time delay is investigated. A stability theorem for linear fractional-order systems with time …