Abstract
The probability distribution of a complexity measure is used to characterize chaotic states: an estimator of the algorithmic complexity of a time series of symbolic words is calculated within a fixed length time window, which sweeps through the time series analyzed. The words are derived through a symbolic dynamics scheme applied in an m-dimensional delay coordinate space. Time intervals instead of the variables of the system are used. The chaotic states of a model of a magnetic domain wall are characterized better by the methods presented than with the use of fractal dimensions and new intermittent states of the system were easily identified. Using an artificial nonstationary signal composed of different chaotic states of the Bloch wall as a test for chaos-chaos intermittency we demonstrate that the method developed is suitable for the detection and characterization of intermittency. It is also shown that nonstationarity in the form of a slow monotonic drift in the control parameter may extend the stability range of periodic states of the spatially extended system studied-a trackinglike phenomenon.
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