Multi-almost periodicity and invariant basins of general neural networks under almost periodic stimuli

Details Multi-almost periodicity and invariant basins of general neural networks under almost periodic stimuli Multi-almost periodicity and invariant basins of general neural networks under almost periodic stimuli

2009-11-19

arxiv.org/abs/0911.3713v1

Mathematics

Dynamical Systems

28 pages, 4 figures

Scientific paper

10.1002/cta.490

In this paper, we investigate convergence dynamics of $2^N$ almost periodic encoded patterns of general neural networks (GNNs) subjected to external almost periodic stimuli, including almost periodic delays. Invariant regions are established for the existence of $2^N$ almost periodic encoded patterns under two classes of activation functions. By employing the property of $\mathscr{M}$-cone and inequality technique, attracting basins are estimated and some criteria are derived for the networks to converge exponentially toward $2^N$ almost periodic encoded patterns. The obtained results are new, they extend and generalize the corresponding results existing in previous literature.

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