Topics in nonlinear time series analysis : with implications for EEG analysis / Andreas Galka
Tipo de material:
TextoSeries Advanced series in nonlinear dynamics ; 14Detalles de publicación: Singapore : World Scientific, 2000 Descripción: XV, 342 p. : il. ; 23 cmISBN: 981-02-4148-8Tema(s): Teorías no lineales | Análisis de series temporalesResumen: This book provides a thorough review of a class of powerful algorithms for the numerical analysis of complex time series data which were obtained from dynamical systems. These algorithms are based on the concept of state space representations of the underlying dynamics, as introduced by nonlinear dynamics. In particular, current algorithms for state space reconstruction, correlation dimension estimation, testing for determinism and surrogate data testing are presented -- algorithms which have been playing a central role in the investigation of deterministic chaos and related phenomena since 1980. Special emphasis is given to the much-disputed issue whether these algorithms can be successfully employed for the analysis of the human electroencephalogram.
| Tipo de ítem | Biblioteca de origen | Signatura | URL | Estado | Fecha de vencimiento | Código de barras | Reserva de ítems |
|---|---|---|---|---|---|---|---|
| Monografías | 03. BIBLIOTECA INGENIERÍA PUERTO REAL | 517.938/GAL/top (Navegar estantería(Abre debajo)) | Texto completo | Prestado | 31/01/2025 | 374137628X |
Indice
Bibliografía: p. 321-335
This book provides a thorough review of a class of powerful algorithms for the numerical analysis of complex time series data which were obtained from dynamical systems. These algorithms are based on the concept of state space representations of the underlying dynamics, as introduced by nonlinear dynamics. In particular, current algorithms for state space reconstruction, correlation dimension estimation, testing for determinism and surrogate data testing are presented -- algorithms which have been playing a central role in the investigation of deterministic chaos and related phenomena since 1980. Special emphasis is given to the much-disputed issue whether these algorithms can be successfully employed for the analysis of the human electroencephalogram.
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