Robust chaos generation by a perceptron
Minerva Center and Department of Physics,
Bar-Ilan University 52900 Ramat-Gan, Israel
Accepted: 19 May 2000
The properties of time series generated by a perceptron with monotonic and non-monotonic transfer function, where the next input vector is determined from past output values, are examined. The analysis of the parameter space reveals the following main finding: a perceptron with a monotonic function can produce fragile chaos only, whereas a non-monotonic function can generate robust chaos as well. For non-monotonic functions, the dimension of the attractor can be controlled monotonically by tuning a natural parameter in the model.
PACS: 84.35.+i – Neural networks / 05.45.-a – Nonlinear dynamics and nonlinear dynamical systems
© EDP Sciences, 2000