A technique to discriminate complex signals associated with deterministic chaos from those of random origin is presented. The method applies a new, computationally efficient, prediction procedure based on the associative memory concept. This procedure was used as a method to model non-linear series. Its performance was analyzed for several time series, including simulated numerical data from the logistic map and from the Mackey-Glass delay equation. Also experimental data from a dripping faucet in a chaotic regime and from low-temperature thermal fluctuations of the voltage across a resistor are considered. © 1992.
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