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Sampled-data state estimation for neural networks of neutral type. (English) Zbl 1343.92032

Summary: In this paper, the sampled-data state estimation is investigated for a class of neural networks of neutral type. By employing a suitable Lyapunov functional, a delay-dependent criterion is established to guarantee the existence of the sampled-data estimator. The estimator gain matrix can be obtained by solving linear matrix inequalities (LMIs). A numerical example is given to show the effectiveness of the proposed method.

MSC:

92B20 Neural networks for/in biological studies, artificial life and related topics
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