The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Delay-independent stability in bidirectional associative memory networks
Abstract
It is shown that if the neuronal gains are small compared with the synaptic connection weights, then a bidirectional associative memory network with axonal signal transmission delays converges to the equilibria associated with exogenous inputs to the network. Both discrete and continuously distributed delays are considered; the asymptotic stability is global in the state space of neuronal activations and also is independent of the delays.
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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
- Gopalsamy, K., & He, X.-Z.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1994
- Volume
- 5
- Issue
- 6
- Pages
- 998-1002
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2100337613
- Cited by
- 560
- References
- 1
- FWCI
- 3.48
- Type
- journal article
- Language
- en
- Open access
- closed
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- Gopalsamy, K., & He, X.-Z. (1994). Delay-independent stability in bidirectional associative memory networks. IEEE Transactions on Neural Networks. https://doi.org/10.1109/72.329700
Data sources
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).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
