Deakin University
Browse

File(s) under permanent embargo

Delay-interval-dependent robust-stability criteria for neutral stochastic neural networks with polytopic and linear fractional uncertainties

journal contribution
posted on 2011-07-01, 00:00 authored by P Balasubramaniam, S Lakshmanan
In this paper, the delay-interval-dependent robust stability is studied for a class of neutral stochastic neural networks with time-varying delays. The time-varying delay is assumed to belong to an interval, which means that the upper bound is known and the lower bound is not restricted to zero. For the neural networks under study, the uncertainty includes polytopic uncertainty and linear fractional norm-bounded uncertainty. Sufficient conditions for the stability of the addressed neutral stochastic neural networks with time-varying delays are established by employing the proper Lyapunov-Krasovskii functional, a combination of the stochastic analysis theory, some inequality techniques and new linear matrix inequality (LMI). Finally, three numerical examples are provided to demonstrate less conservatism and effectiveness of the proposed LMI conditions.

History

Journal

International journal of computer mathematics

Volume

88

Pagination

2001-2015

Location

Abingdon, Eng.

ISSN

0020-7160

eISSN

1029-0265

Language

eng

Publication classification

CN.1 Other journal article

Copyright notice

2011, Taylor & Francis

Issue

10

Publisher

Taylor & Francis