Learning chaotic dynamics under noise with on-line EM algorithm
✍ Scribed by Wako Yoshida; Shin Ishii; Masa-aki Sato
- Publisher
- John Wiley and Sons
- Year
- 2001
- Tongue
- English
- Weight
- 304 KB
- Volume
- 84
- Category
- Article
- ISSN
- 1042-0967
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✦ Synopsis
In this article, we discuss the learning of chaotic dynamics by using a normalized Gaussian network (NGnet). The NGnet is trained by an on-line EM algorithm 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. The trained NGnet also shows good prediction performance. When only part of the dynamical variables are observed, the NGnet is trained to learn the vector field in the delay coordinate space. It is shown that the chaotic dynamics is able to be learned with this method under the two kinds of noise.