Robust chaos generation by a perceptron
A. Priel, I. Kanter
Abstract
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. 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.
Create a lesson
Related papers
Orbital cancellation and reentrant localization in a quasiperiodic trimer ladder
Xin Wang, Pei-Jie Chang, Quanfeng Lu et al.
Quantum Localization in Geometric Complex Networks
Jan Lewenstein-Sanpera, Tobias Grass, M. Àngeles Serrano et al.
Eigenstate transitions and critical transport in 3D Aubry-André model
Pragna Das, Lev Vidmar, Miroslav Hopjan
Weyl's theorem and the theoretical origin of Boson peak in two-dimensional scalar spring network
Cunyuan Jiang
On physical implementability of reinforcement-learning-driven reasoning
Eiji Konishi
The Stone-Wales trefoil: a new topological defect in graphene
M. V. Kondrin, Y. B. Lebed, V. V. Brazhkin