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Exponential state estimation for delayed recurrent neural networks with sampled-data

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Abstract

In this paper, the sampled-data state estimation problem is investigated for a class of recurrent neural networks with time-varying delay. Instead of the continuous measurement, the sampled measurement is used to estimate the neuron states, and a sampled-data estimator is constructed. By converting the sampling period into a bounded time-varying delay, the error dynamics of the considered neural network is derived in terms of a dynamic system with two different time-delays. Subsequently, by choosing an appropriate Lyapunov functional and using the Jensen’s inequality, a sufficient condition depending on the sampling period is obtained under which the resulting error system is exponentially stable. Then a sampled-data estimator is designed in terms of the solution to a set of linear matrix inequalities (LMIs) which can be solved by using available software. Finally, a numerical example is employed to demonstrate the effectiveness of the proposed sampled-data estimation approach.

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Correspondence to Nan Li.

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Li, N., Hu, J., Hu, J. et al. Exponential state estimation for delayed recurrent neural networks with sampled-data. Nonlinear Dyn 69, 555–564 (2012). https://doi.org/10.1007/s11071-011-0286-x

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  • DOI: https://doi.org/10.1007/s11071-011-0286-x

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