非线性时间序列分析
1999-12
东南大学出版社
Cees Diks 著
209
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.
ForewordI Introduction2 Nonlinear Dynamical Systems3 Stochastic Time Series4 A Test for Reversibility5 Detecting Differences between Reconstruction Measures6 Estimating Invariants of Noisy Attractors7 The Correlation Integral of Noisy Attractors8 Spiral Wave Tip Dynamics9 Spatio-temporal Chaos: a Solvable ModelAppendix AAppendix BAppendix CReferencesIndex
