Reduced-order state estimation of delayed recurrent neural networks

Different from the widely-studied full-order state estimator design, this paper focuses on dealing with the reduced-order state estimation problem for delayed recurrent neural networks. By employing an integral inequality, a delay-dependent design approach is proposed, and global asymptotical stability of the resulting error system is guaranteed. It is shown that the gain matrix of the reduced-order state estimator is determined by the solution of a linear matrix inequality. Numerical examples are provided to illustrate the effectiveness of the developed result.

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