Mean almost periodicity and moment exponential stability of discrete-time stochastic shunting inhibitory cellular neural networks with time delays

Title:

Mean almost periodicity and moment exponential stability of discrete-time stochastic shunting inhibitory cellular neural networks with time delays

Creator:

Zhang, Tianwei and Xu, Lijun

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:ddf8608e-8dc2-4bf5-acc2-118376f1bb46
uuid:ddf8608e-8dc2-4bf5-acc2-118376f1bb46
doi:10.14736/kyb-2019-4-0690

Subject:

semi-discrete method, stochastic, Krasnoselskii's fixed point theorem, almost periodicity, and global exponential stability

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

By using the semi-discrete method of differential equations, a new version of discrete analogue of stochastic shunting inhibitory cellular neural networks (SICNNs) is formulated, which gives a more accurate characterization for continuous-time stochastic SICNNs than that by Euler scheme. Firstly, the existence of the 2pth mean almost periodic sequence solution of the discrete-time stochastic SICNNs is investigated with the help of Minkowski inequality, Hölder inequality and Krasnoselskii's fixed point theorem. Secondly, the moment global exponential stability of the discrete-time stochastic SICNNs is also studied by using some analytical skills and the proof of contradiction. Finally, two examples are given to demonstrate that our results are feasible. By numerical simulations, we discuss the effect of stochastic perturbation on the almost periodicity and global exponential stability of the discrete-time stochastic SICNNs.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Coverage:

690-713

Source:

Kybernetika | 2019 Volume:55 | Number:4

Harvested from:

CDK

Metadata only:

false