Generation of unpredictable time series by a neural network
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.
- Publication:
-
Physical Review E
- Pub Date:
- May 2001
- DOI:
- 10.1103/PhysRevE.63.056126
- arXiv:
- arXiv:cond-mat/0011302
- Bibcode:
- 2001PhRvE..63e6126M
- Keywords:
-
- 84.35.+i;
- 05.45.Tp;
- Neural networks;
- Time series analysis;
- Condensed Matter - Disordered Systems and Neural Networks
- E-Print:
- 11 pages, 14 figures