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An LMI approach to stability analysis of stochastic high-order Markovian jumping neural networks with mixed time delays

journal contribution
posted on 06.12.2017, 00:00 authored by Yurong LiuYurong Liu, Z Wang, X Liu
This paper deals with the problem of global exponential stability for a general class of stochastic high-order neural networks with mixed time delays and Markovian jumping parameters. The mixed time delays under consideration comprise both discrete time-varying delays and distributed time-delays. The main purpose of this paper is to establish easily verifiable conditions under which the delayed high-order stochastic jumping neural network is exponentially stable in the mean square in the presence of both mixed time delays and Markovian switching. By employing a new Lyapunov-Krasovskii functional and conducting stochastic analysis, a linear matrix inequality (LMI) approach is developed to derive the criteria ensuring exponential stability. Furthermore, the criteria are dependent on both the discrete time-delay and distributed time-delay, and hence less conservative. The proposed criteria can be readily checked by using some standard numerical packages such as the Matlab LMI Toolbox. A simple example is provided to demonstrate the effectiveness and applicability of the proposed testing criteria.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Volume

2

Issue

1

Start Page

110

End Page

120

Number of Pages

11

ISSN

1751-570X

Location

Oxon, England

Publisher

Elseview Sci. Ltd.

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Brunel University; Institute for Resource Industries and Sustainability (IRIS); Yangzhou da xue;

Era Eligible

Yes

Journal

Nonlinear Analysis: Hybrid Systems.