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Math. Comput. Appl. 2013, 18(1), 50-61; doi:10.3390/mca18010050

Anti-Periodic Solutions for Neural Networks with Delays and Impulses

Department of Mathematics, Taiyuan University of Technology, 030024,Taiyuan , Shanxi, China
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Published: 1 April 2013
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Abstract

In this paper we investigate a class of artificial neural networks with delays subject to periodic impulses. By exploiting Lyapunov functions, we analyze the global exponential stability of an arbitrary solution with initial value being bounded by Υ . Further, we discuss the existence of anti-periodic solutions by constructing fundamental function sequences based on a solution with initial value being bounded by γ . We also establish sufficient conditions to ensure the existence, uniqueness and exponential stability of anti-periodic solutions, which are new and easily verifiable. At last, we present a network with its time-series and phase graphics to demonstrate our results.
Keywords: artificial neural networks (ANN); anti-periodic solutions; delays and impulses; exponential stability artificial neural networks (ANN); anti-periodic solutions; delays and impulses; exponential stability
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Shi, P.; Dong, L. Anti-Periodic Solutions for Neural Networks with Delays and Impulses. Math. Comput. Appl. 2013, 18, 50-61.

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