4.4 Article

Mean Square Exponential Stability for Uncertain Delayed Stochastic Neural Networks with Markovian Jump Parameters

Journal

CIRCUITS SYSTEMS AND SIGNAL PROCESSING
Volume 29, Issue 2, Pages 331-348

Publisher

SPRINGER BIRKHAUSER
DOI: 10.1007/s00034-009-9138-z

Keywords

Neural networks; Stochastic systems; Markovian jump parameters; Time-varying delays; LMIs

Funding

  1. Natural Science Foundation of China [60674055, 60774047]
  2. Taishan Scholar Programme of Shandong Province

Ask authors/readers for more resources

This paper is concerned with the problem of delay-dependent mean square exponential stability for a class of delayed stochastic Hopfield neural networks with Markovian jump parameters. The delays here are time-varying delays. Based on a new Lyapunov-Krasovskii functional, delay-dependent stability conditions are derived by means of linear matrix inequalities (LMIs). It is shown that the proposed results can contain some existing stability conditions as a special case. Finally, three numerical examples are given to illustrate the effectiveness of the proposed method, and the simulations show that our results are less conservative than the existing ones.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.4
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available