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A method of oscillatory trajectory generation using recurrent hybrid neural networks

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.