Fractional Maps as Maps with Power-Law Memory

TL;DR

This paper introduces fractional maps with power-law memory derived from fractional differential equations, revealing complex dynamics such as chaos and bifurcations, applicable to various physical and biological systems.

Contribution

It develops the concept of $oldsymbol{ ext{α}}$-families of maps to model systems with power-law memory, expanding the understanding of nonlinear fractional dynamical systems.

Findings

Phase space contains periodic sinks and chaotic attractors.

Discovery of attracting slow diverging and accelerator mode trajectories.

New properties of attractors in fractional systems compared to regular dynamics.

Abstract

The study of systems with memory requires methods which are different from the methods used in regular dynamics. Systems with power-law memory in many cases can be described by fractional differential equations, which are integro-differential equations. To study the general properties of nonlinear fractional dynamical systems we use fractional maps, which are discrete nonlinear systems with power-law memory derived from fractional differential equations. To study fractional maps we use the notion of $\alpha$-families of maps depending on a single parameter $\alpha > 0$ which is the order of the fractional derivative in a nonlinear fractional differential equation describing a system experiencing periodic kicks. $\alpha$-families of maps represent a very general form of multi-dimensional nonlinear maps with power-law memory, in which the weight of the previous state at time $t_i$ in defining the present state at time $t$ is proportional to $(t-{t_i})^{\alpha-1}$. They may be applicable to studying some systems with memory such as viscoelastic materials, electromagnetic fields in dielectric media, Hamiltonian systems, adaptation in biological systems, human memory, etc. Using the fractional logistic and standard $\alpha$-families of maps as examples we demonstrate that the phase space of nonlinear fractional dynamical systems may contain periodic sinks, attracting slow diverging trajectories, attracting accelerator mode trajectories, chaotic attractors, and cascade of bifurcations type trajectories %with some new properties. whose properties are different from properties of attractors in regular dynamical systems.