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 …

This paper investigates a Hopfield neural network under the simulation of external
electromagnetic radiation and dual bias currents, in which the fluctuation of magnetic flux …

In this research, we offer a novel discrete-time fractional Hopfield neural network (DTHNN)
with incommensurate order. Analytical and numerical analysis by employing maximum …

This work principally probes into the stability, the existence, and control of Hopf bifurcation of
new established fractional‐order delayed bidirectional associative memory (BAM) neural …

The multiple activities of neurons frequently generate several spiking-bursting variations
observed within the neurological mechanism. We show that a discrete fractional-order …

This paper describes a new four-dimensional fractional discrete neural network with
electromagnetic radiation model. In addition, the non-linear dynamics of the suggested …

In this paper, a neural network approach is suggested to the product design analysis.
Namely, fractional-order neural network models are proposed as more flexible mechanism …

This article investigates finite-time stability (FTS) and finite-time contractive stability (FTCS)
of discontinuous nonlinear fractional-order (FO) systems with time-delay and state …

In this paper, the robust stabilization and synchronization of a novel chaotic system are
presented. First, a novel chaotic system is presented in which this system is realized by …

At present, the extreme multistability of fractional order neural networks are gaining much
interest from researchers. In this paper, by utilizing the fractional ℑ-Caputo operator, a …