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State estimation for delayed Markovian jumping neural networks over sensor nonlinearities and disturbances | IEEE Conference Publication | IEEE Xplore

State estimation for delayed Markovian jumping neural networks over sensor nonlinearities and disturbances


Abstract:

This paper is concerned with the exponential state estimation issue for a class of delayed Markovian jumping neural networks (MJNNs) with sensor nonlinearities and distur...Show More

Abstract:

This paper is concerned with the exponential state estimation issue for a class of delayed Markovian jumping neural networks (MJNNs) with sensor nonlinearities and disturbances. The parameters and discrete delays of the neural networks are subject to the switching from one mode to another according to a Markov chain. By constructing a novel Lyapunov-Krasovskii functional, a mode-dependent exponential stability condition is proposed, such that the resulting estimation error system is exponentially stable in the mean square. The design of the desired state estimator is derived by solving a set of linear matrix inequalities (LMIs). Finally, a numerical example is given to illustrate the validity of the theoretical results.
Date of Conference: 29 October 2017 - 01 November 2017
Date Added to IEEE Xplore: 18 December 2017
ISBN Information:
Conference Location: Beijing, China

References

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