The detection of an underlying chaotic behavior in experimental recordings is a longstanding issue in the field of nonlinear time series analysis. Conventional approaches require the assessment of a suitable dimension and lag pair to embed a given input sequence and, thereupon, the estimation of dynamical invariants to characterize the underlying source. In this work, we propose an alternative approach to the problem of identifying chaos, which is built upon an improved method for optimal embedding. The core of the new approach is the analysis of an input sequence on a lattice of embedding pairs whose results provide, if any, evidence of a finite-dimensional, chaotic source generating the sequence and, if such evidence is present, yield a set of equivalently suitable embedding pairs to embed the sequence. The application of this approach to two experimental case studies, namely, an electronic circuit and magnetoencephalographic recordings of the human brain, highlights how it can make up a powerful tool to detect chaos in complex systems. Published under license by AIP Publishing.

Chasing chaos by improved identification of suitable embedding dimensions and lags / Perinelli, Alessio; Ricci, Leonardo. - In: CHAOS. - ISSN 1054-1500. - ELETTRONICO. - 30:12(2020), pp. 123104.1-123104.13. [10.1063/5.0029333]

Chasing chaos by improved identification of suitable embedding dimensions and lags

Perinelli, Alessio;Ricci, Leonardo
2020-01-01

Abstract

The detection of an underlying chaotic behavior in experimental recordings is a longstanding issue in the field of nonlinear time series analysis. Conventional approaches require the assessment of a suitable dimension and lag pair to embed a given input sequence and, thereupon, the estimation of dynamical invariants to characterize the underlying source. In this work, we propose an alternative approach to the problem of identifying chaos, which is built upon an improved method for optimal embedding. The core of the new approach is the analysis of an input sequence on a lattice of embedding pairs whose results provide, if any, evidence of a finite-dimensional, chaotic source generating the sequence and, if such evidence is present, yield a set of equivalently suitable embedding pairs to embed the sequence. The application of this approach to two experimental case studies, namely, an electronic circuit and magnetoencephalographic recordings of the human brain, highlights how it can make up a powerful tool to detect chaos in complex systems. Published under license by AIP Publishing.
2020
12
Perinelli, Alessio; Ricci, Leonardo
Chasing chaos by improved identification of suitable embedding dimensions and lags / Perinelli, Alessio; Ricci, Leonardo. - In: CHAOS. - ISSN 1054-1500. - ELETTRONICO. - 30:12(2020), pp. 123104.1-123104.13. [10.1063/5.0029333]
File in questo prodotto:
File Dimensione Formato  
Chaos_2020_30_123104_Perinelli_Ricci.pdf

Solo gestori archivio

Descrizione: Articolo scientifico
Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 7.61 MB
Formato Adobe PDF
7.61 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/283317
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 8
  • ???jsp.display-item.citation.isi??? 7
  • OpenAlex ND
social impact