非线性时间序列分析：方法及应用NONLINEAR TIME SERIES ANALYSIS

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.

Foreword
I Introduction
2 Nonlinear Dynamical Systems
3 Stochastic Time Series
4 A Test for Reversibility
5 Detecting Differences between Reconstruction Measures
6 Estimating Invariants of Noisy Attractors
7 The Correlation Integral of Noisy Attractors
8 Spiral Wave Tip Dynamics
9 Spatio-temporal Chaos: a Solvable Model
Appendix A
Appendix B
Appendix C
References
Index