Generation of unpredictable time series by a Neural Network
Disordered Systems and Neural Networks
2009-10-31 v2
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.
Keywords
Cite
@article{arxiv.cond-mat/0011302,
title = {Generation of unpredictable time series by a Neural Network},
author = {Richard Metzler and Wolfgang Kinzel and Liat Ein-Dor and Ido Kanter},
journal= {arXiv preprint arXiv:cond-mat/0011302},
year = {2009}
}
Comments
11 pages, 14 figures; slightly expanded and clarified, mistakes corrected; accepted for publication in PRE