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

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.

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