The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Global Attractivity in Delayed Hopfield Neural Network Models
Abstract
Two different approaches are employed to investigate the global attractivity of delayed Hopfield neural network models. Without assuming the monotonicity and differentiability of the activation functions, Liapunov functionals and functions (combined with the Razumikhin technique) are constructed and employed to establish sufficient conditions for global asymptotic stability independent of the delays. In the case of monotone and smooth activation functions, the theory of monotone dynamical systems is applied to obtain criteria for global attractivity of the delayed model. Such criteria depend on the magnitude of delays and show that self-inhibitory connections can contribute to the global convergence.
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Citations
Cites
Hopfield, J. J. (1984) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.81.10.3088 · Cited by 6,918
Cited by
Global asymptotic stability of a general class of recurrent neural networks with time-varying delays
Cao, J., & Wang, J. (2003) · IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications · doi:10.1109/tcsi.2002.807494 · Cited by 555
Metadata
- Authors
- van den Driessche, P., & Zou, X.
- Venue
- SIAM Journal on Applied Mathematics
- Published
- 1998-12
- Volume
- 58
- Issue
- 6
- Pages
- 1878-1890
- ISSN
- 0036-1399 (ISSN-L)
- 1095-712X
- OpenAlex
- W2084820442
- Cited by
- 428
- References
- 1
- FWCI
- 15.54
- Type
- journal article
- Language
- en
- Open access
- closed
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- van den Driessche, P., & Zou, X. (1998). Global Attractivity in Delayed Hopfield Neural Network Models. SIAM Journal on Applied Mathematics. https://doi.org/10.1137/s0036139997321219
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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