Entropies from Markov Models as Complexity Measures of Embedded Attractors

TL;DR

This paper introduces three new entropy estimators based on Markov models to quantify the complexity and stability of embedded attractors in dynamical systems, aiding in the analysis of their behavioral changes.

Contribution

It proposes novel entropy estimation methods derived from Markov models for assessing the complexity of embedded attractors.

Findings

New entropy estimators effectively measure attractor stability.

Methods distinguish different dynamical behaviors.

Enhanced characterization of system complexity.

Abstract

This paper addresses the problem of measuring complexity from embedded attractors as a way to characterize changes in the dynamical behaviour of different types of systems by observing their outputs. With the aim of measuring the stability of the trajectories of the attractor along time, this paper proposes three new estimations of entropy that are derived from a Markov model of the embedded attractor.