Global Exponential Stability for DCNNs with Impulses on Time Scales

Yongkun Li and Yuanhong Zhi

Mathematical Problems in Engineering, 2014, vol. 2014, 1-10

Abstract:

A class of delayed cellular neural networks (DCNNs) with impulses on time scales is considered. By using the topological degree theory, and the time scale calculus theory some sufficient conditions are derived to ensure the existence, uniqueness, and global exponential stability of equilibria for this class of neural networks. Finally, a numerical example illustrates the feasibility of our results and also shows that the continuous-time neural network and the discrete-time analogue have the same dynamical behaviors. The results of this paper are completely new and complementary to the previously known results.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:934592

DOI: 10.1155/2014/934592

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