Publicação
Convergence of asymptotic systems in Cohen-Grossberg neural network models with unbounded delays
| Resumo: | In this paper, we investigate the convergence of asymptotic systems in nonautonomous Cohen–Grossberg neural network models, which include both infinite discrete time-varying and distributed delays. We derive stability results under conditions where the non-delay terms asymptotically dominate the delay terms. Several examples and a numerical simulation are provided to illustrate the significance and novelty of the main result. |
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| Autores principais: | Elmwafy, Ahmed |
| Outros Autores: | Oliveira, José J.; Silva, César M. |
| Assunto: | Cohen–Grossberg neural network Global convergence Asymptotic systems Infinite discrete delay Infinite distributed delay Ciências Naturais::Matemáticas C: Mathematical and Quantitative Methods |
| Ano: | 2026 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | In this paper, we investigate the convergence of asymptotic systems in nonautonomous Cohen–Grossberg neural network models, which include both infinite discrete time-varying and distributed delays. We derive stability results under conditions where the non-delay terms asymptotically dominate the delay terms. Several examples and a numerical simulation are provided to illustrate the significance and novelty of the main result. |
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