According to a study from Nanjing, People's Republic of China, "This paper is concerned with the state estimation problem for a class of discrete-time stochastic neural networks (DSNNs) with random delays. The effect of both variation range and distribution probability of the time delay are taken into account in the proposed approach."
"The stochastic disturbances are described in terms of a Brownian motion and the time-varying delay is characterized by introducing a Bernoulli stochastic variable. By employing a Lyapunov-Krasovskii functional, sufficient delay-distribution-dependent conditions are established in terms of linear matrix inequalities (LMIs) that guarantee the existence of the state estimator which can be checked readily by the Matlab toolbox. The main feature of the results obtained in this paper is that they are dependent on not only the bound but also the distribution probability of the time delay, and we obtain a larger allowance variation range of the delay, hence our results are less conservative than the traditional delay-independent ones," wrote H.B. Bao and colleagues.
The researchers concluded: "One example is given to illustrate the effectiveness of the proposed result."
Bao and colleagues published their study in Neural Networks (Delay-distribution-dependent state estimation for discrete-time stochastic neural networks with random delay. Neural Networks, 2011;24(1):19-28).
For more information, contact J.D. Cao, Southeast University, Dept. of Math, Nanjing 210096, People's Republic of China.
Publisher contact information for the journal Neural Networks is: Pergamon-Elsevier Science Ltd., the Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England.
Keywords: City:Nanjing, Country:People's Republic of China, Region:Asia, Neural Networks
This article was prepared by Internet Networks & Communications editors from staff and other reports. Copyright 2011, Internet Networks & Communications via VerticalNews.com.
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