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Time series analysis for system identification and diagnostics. (English) Zbl 0717.92002

The reasoning is based on the heuristic argument that a very small set of observations obtained from a natural dynamic system contains sufficient information about the underlying mathematical dynamic system, even though the observations are of finite precision and contain noise.
The authors use time series, filter them for noise in order to ‘ascertain’ the presence of self-generated oscillations, and then estimate some numerical characteristics of the corresponding attractor (mainly its dimension and Lyapunov exponent). Two types of statistical analyses are compared with respect to additive and non-additive noise.
Reviewer: I.Gumowski

MSC:

92B05 General biology and biomathematics
62M20 Inference from stochastic processes and prediction
92-08 Computational methods for problems pertaining to biology
37D45 Strange attractors, chaotic dynamics of systems with hyperbolic behavior
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