Global exponential stability of discrete-time Hopfield neural network models with unbounded delays
In this paper, a general setting is presented to study the exponential stability of discrete-time systems with bounded or unbounded delays. Based on the M-matrix theory, we establish sufficient conditions to ensure the global exponential stability of the zero equilibrium of low-order, and high-order...
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| Formato: | article |
| Idioma: | eng |
| Publicado em: |
2022
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| Texto completo: | https://hdl.handle.net/1822/78376 |
| País: | Portugal |
| Oai: | oai:repositorium.sdum.uminho.pt:1822/78376 |
| Resumo: | In this paper, a general setting is presented to study the exponential stability of discrete-time systems with bounded or unbounded delays. Based on the M-matrix theory, we establish sufficient conditions to ensure the global exponential stability of the zero equilibrium of low-order, and high-order, discrete-time Hopfield neural network models with unbounded delays and delay in the leakage terms. A comparison of the literature shows that our results generalize and improve some in recent publications. |
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