Generation of unpredictable time series by a neural network.
A perceptron that "learns" the opposite of its own output is used to generate a time series. We analyze properties of the weight vector and the generated sequence, such as the cycle length and the probability distribution of generated sequences. A remarkable suppression of the autocorrelation function is explained, and connections to the Bernasconi model are discussed. If a continuous transfer function is used, the system displays chaotic and intermittent behavior, with the product of the learning rate and amplification as a control parameter.
Metzler
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Institut für Theoretische Physik, Universität Würzburg, Am Hubland, D-97074 Würzburg, Germany.
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Ein-Dor
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