Learning chaotic dynamics under noise with on-line EM algorithm

In this article, we discuss the learning of chaotic dynamics by using a nor malized Gaussian network (NGnet). The NGnet is trained by an on-line EM alg orithm in order to learn the vector field of the chaotic dynamics. We also investigate the robustness of our approach to two kinds of noise processes: system noise and observation noise. It is shown that the trained NGnet is able to reproduce a chaotic attractor, even under the two kinds of noise. T he trained NGnet also shows good prediction performance. When only part of the dynamical variables are observed, the NGnet is trained to learn the vec tor field in the delay coordinate space. It is shown that the chaotic dynam ics is able to be learned with this method under the two kinds of noise. (C ) 2001 Scripta Technica.