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Almost periodic solution of recurrent neural networks with backward shift operators on time scales

Lili Wang, Meng Hu

Abstract



In this paper, a class of recurrent neural networks (RNNs) with backward shift operators are studied on almost periodic time scales, some sufficient conditions are established for the existence and global exponential stability of the almost periodic solution. These results have important leading significance in designs and applications of RNNs. Finally, two examples and numerical simulations are presented to illustrate the feasibility and effectiveness of the results.

Keywords


Recurrent neural network; Almost periodic solution; Global exponential stabili- ty; Time scale.

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