A method of oscillatory trajectory generation using recurrent hybrid neural networks
Yasuaki Kuroe, Kotaro MIURA · Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. · 2006
In the biological systems there are numerous examples of autonomously generated periodic activities. Several different periodic patterns are generated simultaneously in one living body. This paper discusses a problem of generating periodic oscillatory trajectories in an artificial neural network. We propose a learning method of a neural network such that it possesses desired autonomous periodic trajectories. Especially a method to generate not only one periodic trajectory but also two or more different trajectories simultaneously at specified positions in the state space of a neural network. For this purpose we utilize a class of neural network, recurrent hybrid neural networks and develop efficient learning methods for them. Experimental examples are also presented to demonstrate the applicability and performance of the proposed method.