Approximation Capability of Continuous Time Recurrent Neural Networks for Non-autonomous Dynamical Systems

Authors:
Yuichi Nakamura;Masahiro Nakagawa
Affiliations:
Nagaoka University of Technology, Nagaoka-shi, Japan 940-2188;Nagaoka University of Technology, Nagaoka-shi, Japan 940-2188
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Year:
2009

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Abstract

The main goal of this study is to elucidate the theoretical capability of the continuous time recurrent neural network. In this paper, we show that the approximation capability of the continuous time recurrent network can be extended to non-autonomous dynamical systems with external inputs. Moreover, if the dynamical system has an asymptotically stable periodic solution for a periodic external input, it is shown that the approximation can be extended to the global time interval.