Abstract
This paper is concerned with the L2–L∞ performance state estimation problem of delayed neural networks. Firstly, the second-order Bessel–Legendre inequality based on reciprocally convex approach is proposed. Secondly, based on the improved integral inequality, a new delay-dependent condition is derived, which ensures the asymptotic stability of estimation error system with L2–L∞ performance. As a result, the estimator gain matrix and the optimal L2–L∞ performance level are obtained. Simulation results are finally shown to illustrate the effectiveness of the proposed approach.
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