Global exponential stability of nonautonomous neural network models with continuous distributed delays
| Autor: | Elçin Gökmen, José J. Oliveira, Salete Esteves |
|---|---|
| Přispěvatelé: | Universidade do Minho |
| Rok vydání: | 2013 |
| Předmět: |
Equilibrium point
Science & Technology Artificial neural network Differential equation Applied Mathematics 010102 general mathematics Time-varying coefficient 02 engineering and technology Type (model theory) BAM neural network 01 natural sciences Hopfield neural network Distributed time delay Computational Mathematics Exponential stability Periodic solution Control theory Global exponential stability 0202 electrical engineering electronic engineering information engineering Applied mathematics 020201 artificial intelligence & image processing 0101 mathematics Mathematics |
| Zdroj: | Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
| ISSN: | 0096-3003 |
| Popis: | For a family of non-autonomous differential equations with distributed delays, we give sufficient conditions for the global exponential stability of an equilibrium point. This family includes most of the delayed models of neural networks of Hopfield type, with time-varying coefficients and distributed delays. For these models, we establish sufficient conditions for their global exponential stability. The existence and global exponential stability of a periodic solution is also addressed. A comparison of results shows that these results are general, news, and add something new to some earlier publications. Fundação para a Ciência e a Tecnologia (FCT) |
| Databáze: | OpenAIRE |
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