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Generation of unpredictable time series by a Neural Network

Richard Metzler·Wolfgang Kinzel·Liat Ein-Dor·Ido Kanter
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Abstract

A perceptron that learns the opposite of its own output is used to generate a time series. We analyse properties of the weight vector and the generated sequence, like 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 behaviour, with the product of the learning rate and amplification as a control parameter.

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Richard Metzler, Wolfgang Kinzel, Liat Ein-Dor, Ido Kanter. 2001-04-03. Generation of unpredictable time series by a Neural Network. https://doi.org/10.1103/physreve.63.056126

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