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 …

The present study aims to design stochastic intelligent computational heuristics for the
numerical treatment of a nonlinear SITR system representing the dynamics of novel …

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 …

Bio-inspired computing approaches are effective to solve a variety of dynamical problems.
The strength of these stochastic solvers is exploited for the numerical treatment of a …

For analysis of physical properties of different materials, rectangular porous fins are used to
examine the heat transformation through a system. In this paper, a metaheuristic is …

Rough volatility models are recently popularized by the need of a consistent model for the
observed empirical volatility in the financial market. In this case, it has been shown that the …

In the present study, stochastic numerical computing approach is developed by applying
artificial neural networks (ANNs) to compute the solution of Lane–Emden type boundary …

This paper mainly discusses a novel conceptual framework: fractional Hopfield neural
networks (FHNN). As is commonly known, fractional calculus has been incorporated into …

In this study, an efficient soft computing paradigm is presented for solving Bagley–Torvik
systems of fractional order arising in fluid dynamic model for the motion of a rigid plate …

In this paper, we approximate the solution of fractional differential equations using a new
approach of artificial neural network. We consider fractional differential equations of variable …