Robust exponential passive filtering for uncertain neutral-type neural networks with time-varying mixed delays via Wirtinger-based integral inequality

This paper is concerned with the problem of the robust exponential passive filter design for uncertain neutral-type neural networks with time-varying mixed delays. Our aim is to design a Luenberger-type filter for estimating information about the neuron states, which is required in some applied areas. By constructing an appropriate Lyapunov-Krasovskii functional and using the Wirtinger-based integral inequality to estimate its derivative, a delay-range-dependent and delay-rate-dependent criterion is presented to ensure the augmented filtering dynamic system to be robustly exponentially stable and passive with an expected dissipation. Since the criterion is presented in the form of linear matrix inequalities with nonlinear constraints, a cone complementarity linearization algorithm is proposed to determine the filter gain from solution to the nonlinear problem. Finally, a numerical example is given to demonstrate the effectiveness of the proposed method.

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