Exponential stability of stochastic interval hopfield neural networks with time-varying delays

Title:

Exponential stability of stochastic interval hopfield neural networks with time-varying delays

Creator:

Li, Xiaolin and Cao, Jinde

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:249de5ce-1db2-468b-9ae4-ce03f2648c8c
uuid:249de5ce-1db2-468b-9ae4-ce03f2648c8c

Subject:

Exponential stability, stochastic interval Hopfield neural networks, time-varying delays, Razumikhin-type theorem, and robust stability

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

In this paper, stochastic interval Hopfield neural networks with time-varying delays are investigated. By applying the Razumikhin-type theorem as well as inequality technique, a set of novel sufficient criteria independent of delays are given for the exponential stability of such networks. As a by-product, for the deterministic Hopfield neural networks with time-varying delays, some delay-independent criteria for their global exponential robust stability are also obtained. The proposed results improve and extend them in the earlier literature and are easier to verify. A numerical example and simulation are also given to illustrate the effectiveness of our results.

Language:

English

Rights:

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

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2007 Volume:17 | Number:1

Harvested from:

CDK

Metadata only:

false