Robust state estimation for neutral-type neural networks with mixed time delays
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Authors
Bo Du
- Department of Mathematics, Huaiyin Normal University, Huaian Jiangsu, 223300, P. R. China.
Wenbing Zhang
- Department of Mathematics, Yangzhou University, Yangzhou Jiangsu, 225002, P. R. China.
Qing Yang
- Department of Mathematics, Huaiyin Normal University, Huaian Jiangsu, 223300, P. R. China.
Abstract
In this paper, the state estimation problem is dealt with a class of neutral-type Markovian neural networks with mixed time
delays. The network systems have a finite number of modes, and the modes may jump from one state to another according to a
Markov chain. We are devoted to design a state estimator to estimate the neuron states, through available output measurements,
such that the dynamics of the estimation error is globally asymptotically stable in the mean square. From the Lyapunov-
Krasovskii functional and linear matrix inequality (LMI) approach, we establish sufficient conditions to guarantee the existence
of the state estimators. Furthermore, it is shown that the traditional stability analysis issue for delayed neural networks with
Markovian chains can be included as a special case of our main results. A simulation shows the usefulness of the derived
LMI-based stability conditions.
Share and Cite
ISRP Style
Bo Du, Wenbing Zhang, Qing Yang, Robust state estimation for neutral-type neural networks with mixed time delays, Journal of Nonlinear Sciences and Applications, 10 (2017), no. 5, 2565--2578
AMA Style
Du Bo, Zhang Wenbing, Yang Qing, Robust state estimation for neutral-type neural networks with mixed time delays. J. Nonlinear Sci. Appl. (2017); 10(5):2565--2578
Chicago/Turabian Style
Du, Bo, Zhang, Wenbing, Yang, Qing. "Robust state estimation for neutral-type neural networks with mixed time delays." Journal of Nonlinear Sciences and Applications, 10, no. 5 (2017): 2565--2578
Keywords
- Neutral-type
- Markovian jumping system
- Lyapunov functional method
- stability.
MSC
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