On asymptotic behavior of state trajectories of piecewise-linear recurrent neural networks generating periodic sequence of binary vectors

Norikazu Takahashi, Yasuhiro Minetoma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently a sufficient condition for the recurrent neural network with the piecewise-linear output characteristic to generate a prescribed periodic sequence of binary vectors such that every two consecutive vectors differ in exactly one component has been derived. If a recurrent neural network satisfies this condition, it is guaranteed that any state trajectory of the network passes through the periodic sequence of regions corresponding to the periodic sequence of binary vectors. However, the asymptotic behavior of the state trajectories has not been clarified yet. In this paper, we study asymptotic behavior of state trajectories of recurrent neural networks satisfying the above-mentioned sufficient condition, and derive a criterion for state trajectories to converge a unique limit cycle.

Original languageEnglish
Title of host publication2008 International Joint Conference on Neural Networks, IJCNN 2008
Pages484-489
Number of pages6
DOIs
Publication statusPublished - Nov 24 2008
Externally publishedYes
Event2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China
Duration: Jun 1 2008Jun 8 2008

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2008 International Joint Conference on Neural Networks, IJCNN 2008
CountryChina
CityHong Kong
Period6/1/086/8/08

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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  • Cite this

    Takahashi, N., & Minetoma, Y. (2008). On asymptotic behavior of state trajectories of piecewise-linear recurrent neural networks generating periodic sequence of binary vectors. In 2008 International Joint Conference on Neural Networks, IJCNN 2008 (pp. 484-489). [4633836] (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2008.4633836