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• W2768093241 abstract "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." @default.
• W2768093241 created "2017-11-17" @default.
• W2768093241 creator A5074290686 @default.
• W2768093241 creator A5084710341 @default.
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• W2768093241 date "2018-02-01" @default.
• W2768093241 modified "2023-10-12" @default.
• W2768093241 title "Reduced-order state estimation of delayed recurrent neural networks" @default.
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• W2768093241 doi "https://doi.org/10.1016/j.neunet.2017.11.002" @default.
• W2768093241 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29179153" @default.
• W2768093241 hasPublicationYear "2018" @default.
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