Identification of chaotic systems with noisy data based on RBF neural networks
Dongmei Li, Fachao Li · 2009
In this paper, we present that noisy chaotic systems can be identified with RBF neural networks. We design three-layers RBF network structure and clarify fundamental properties of RBF networks to learn noisy chaotic systems by some numerical experiments. We also evaluate the identified models with reconstruction of attractors by the identified models. Simulations show that the identified models can approach to original chaotic systems and extract dynamical characteristics of original chaotic systems.