The symbolic partition with generalized Koopman analysis

TL;DR

This paper introduces a novel symbolic partitioning method for chaotic systems using Koopman operator theory, enabling precise analysis of multivariate and complex chaotic time series.

Contribution

The paper proposes the Koopman Analysis (KA) and Generalized Koopman Analysis (GKA) methods, advancing symbolic partitioning by leveraging spectral decomposition of the Koopman operator.

Findings

Successfully applied to unimodal chaotic maps

Identified symbolic boundaries using zero eigenfunctions

Extended applicability to multivariate and hyperchaotic maps

Abstract

Symbolic dynamics serves as a crucial tool in the study of chaotic systems, prompting extensive research into various methods for symbolic partitioning. The limitations of these methods are heuristic and empirical for the partition the multivariate chaotic state space. Notably, the use of operator theory in partitioning the multivariable chaotic series into precise symbolic cells has been underexplored. In this paper, we propose a novel symbolic partition method, referred to as Koopman Analysis(KA) method, exploiting Koopman operator theory to address the symbolic partition, especially multivariate chaotic time series. We map the chaotic time series into the basis functions to obtain the approximate representation of the Koopman operator.Then we transpose the Koopman approximate matrix and subsequently perform spectral decomposition to obtain the Koopman left eigenfunctions. We apply KA method to one-dimensional unimodal chaotic map to obtain Koopman left eigenfunctions. Then we find some particular eigenfunctions whose eigenvalues are zero, some of which can be used to identify the symbolic boundary of region composed of chaotic series in that the oscillation coincides with the subregion where the theoretical symbolic boundary is located. We refer to the function as the Valid Left Eigenfunction with Zero(VLEZ). Based on the number of oscillations, we further classify VLEZ into two categories. Then, we modify the KA method applicable to chaotic localized subregion and further propose the Generalized Koopman Analysis (GKA) method. The KA method can be also applied to the multimodal maps, multivariate chaotic maps and hyperchaotic maps and their noisy version. The current work can be well further expand to higher dimensional and more complex time series due to its interpretability and availability.