Learning Nonlinear Dynamics by Recurrent Neural Networks(Some Problems on the Theory of Dynamical Systems in Applied Sciences)
Masa-aki Sato, Yoshihiko Murakami · Kyoto University Research Information Repository (Kyoto University) · 1991
A recurrent network, which can approximate a universal class of nonlinear dynamic systems, and its learning algorithm are presented.The possibility of learning chaotic dynamics by the recurrent network was investigated.The Lorentz attractor was used as an example of chaotic dynamics.When the trajectory of the Lorentz attractor was used as the teacher signal, the network was able to acquire the time evolution rule of the Lorentz dynamics and generated a chaotic attractor similar to the Lorentz attractor.The possibility of learning the hidden chaotic dynamics was also investigated.