Improved asymptotical stability criteria for static recurrent neural networks
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Bibliographic record
Abstract
Abstract In this paper, the problem of asymptotical stability for static recurrent neural networks is investigated. Based on delay partitioning approach and a new Lyapunov–Krasvoskii functional, delay-independent conditions are proposed to ensure the asymptotic stability of the static recurrent neural networks. The delay-independent conditions are less conservative than the existing ones. Expressed in linear matrix inequalities, the stability conditions can be checked using the standard numerical software. Two numerical examples are provided to illustrate the effectiveness and the reduced conservatism of the proposed results. Keywords: global asymptotical stabilitylinear matrix inequality (LMI)Lyapunov–Krasvoskii functionaldelay partitioning approachrecurrent neural network 2000 AMS Subject Classifications : 34D2037C7539A1170K2093D09 Acknowledgements This work is partly supported by the Nature Science Foundation of Chongqing (CSTC, 2009BB2378, 2008BB2199).
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| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
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| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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