2008
On Asymptotic Behavior of State Trajectories of Piecewise-Linear Recurrent Neural Networks Generating Periodic Sequence of Binary Vectors
2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8
- ,
- First page
- 484
- Last page
- 489
- Language
- English
- Publishing type
- Research paper (international conference proceedings)
- DOI
- 10.1109/IJCNN.2008.4633836
- Publisher
- IEEE
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.
- Link information
- ID information
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- DOI : 10.1109/IJCNN.2008.4633836
- ISSN : 2161-4393
- Web of Science ID : WOS:000263827200079