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Multistability and New Attraction Basins of Almost-Periodic Solutions of Delayed Neural Networks

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3 Author(s)
Lili Wang ; Shanghai Key Lab. for Contemporary Appl. Math., Fudan Univ., Shanghai, China ; Wenlian Lu ; Tianping Chen

In this paper, we investigate multistability of almost-periodic solutions of recurrently connected neural networks with delays (simply called delayed neural networks). We will reveal that under some conditions, the space Rn can be divided into 2n subsets, and in each subset, the delayed n -neuron neural network has a locally stable almost-periodic solution. Furthermore, we also investigate the attraction basins of these almost-periodic solutions. We reveal that the attraction basin of almost-periodic trajectory is larger than the subset, where the corresponding almost-periodic trajectory is located. In addition, several numerical simulations are presented to corroborate the theoretical results.

Published in:

Neural Networks, IEEE Transactions on  (Volume:20 ,  Issue: 10 )