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Generation of unpredictable time series by a neural network

Richard Metzler and Wolfgang Kinzel

Liat Ein-Dor and Ido Kanter

  • Institut für Theoretische Physik, Universität Würzburg, Am Hubland, D-97074 Würzburg, Germany

  • Minerva Center and Department of Physics, Bar-Ilan University, Ramat Gan, 52900 Israel

Phys. Rev. E 63, 056126 – Published 26 April, 2001

DOI: https://doi.org/10.1103/PhysRevE.63.056126

Abstract

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.

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