Convergence of asymptotic systems of non-autonomous neural network models with infinite distributed delays
In this paper we investigate the global convergence of solutions of non-autonomous Hopfield neural network models with discrete time-varying delays, infinite distributed delays, and possible unbounded coefficient functions. Instead of using Lyapunov functionals, we explore intrinsic features between...
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| Format: | article |
| Language: | eng |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/1822/46289 |
| Country: | Portugal |
| Oai: | oai:repositorium.sdum.uminho.pt:1822/46289 |
| Summary: | In this paper we investigate the global convergence of solutions of non-autonomous Hopfield neural network models with discrete time-varying delays, infinite distributed delays, and possible unbounded coefficient functions. Instead of using Lyapunov functionals, we explore intrinsic features between the non-autonomous systems and their asymptotic systems to ensure the boundedness and global convergence of the solutions of the studied models. Our results are new and complement known results in the literature. The theoretical analysis is illustrated with some examples and numerical simulations. |
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