The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Analysis and synthesis of a class of neural networks: linear systems operating on a closed hypercube
Abstract
An investigation was conducted of the qualitative properties of a class of neural networks described by a system of first-order linear ordinary differential equations which are defined on a closed hypercube of the state space with solutions extended to the boundary of the hypercube. When solutions are located on the boundary of the hypercube, the system is said to be in a saturated mode. The class of systems considered retains the basic structure of the Hopfield model but is easier to analyze, synthesize, and implement. An efficient analysis method is developed which can be used to determine completely the set of asymptotically stable equilibrium points and the set of unstable equilibrium points. The latter set can be used to estimate the domains of attraction for the elements of the former set. The class of systems considered can easily be implemented in analog integrated circuits. The applicability of the results is demonstrated by means of several examples.>
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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
Forti, M., & Tesi, A. (1995) · IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications · doi:10.1109/81.401145 · Cited by 745
Metadata
- Authors
- Li, J.-H., Michel, A. N., & Porod, W.
- Venue
- IEEE Transactions on Circuits and Systems
- Published
- 1989
- Volume
- 36
- Issue
- 11
- Pages
- 1405-1422
- ISSN
- 0098-4094 (ISSN-L)
- OpenAlex
- W2100885571
- Cited by
- 249
- References
- 1
- FWCI
- 8.60
- Type
- journal article
- Language
- en
- Open access
- closed
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- Li, J.-H., Michel, A. N., & Porod, W. (1989). Analysis and synthesis of a class of neural networks: linear systems operating on a closed hypercube. IEEE Transactions on Circuits and Systems. https://doi.org/10.1109/31.41297
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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).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
