A reliable and efficient method of distinguishing between chaotic and nonchaotic behavior in noise-contaminated, but essentially stationary and deterministic, time series data has far reaching applications. Recently, we proposed a new method of detecting chaos which applies directly to the time series data and does not require phase space reconstruction. To illustrate the effectiveness of the method for experimental data, we analyze data from a bipolar motor.
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Falconer et al. (2007) studied this question.
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