The DOI matches its Crossref record; 101 of the 101 references it cites were found in a registry or an index.
Progress in supervised neural networks
Abstract
Theoretical results concerning the capabilities and limitations of various neural network models are summarized, and some of their extensions are discussed. The network models considered are divided into two basic categories: static networks and dynamic networks. Unlike static networks, dynamic networks have memory. They fall into three groups: networks with feedforward dynamics, networks with output feedback, and networks with state feedback, which are emphasized in this work. Most of the networks discussed are trained using supervised learning.>
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Cites
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Forecasting with artificial neural networks:
Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998) · International Journal of Forecasting · doi:10.1016/s0169-2070(97)00044-7 · Cited by 4,229
An introduction to multisensor data fusion
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Machine learning based solutions for security of Internet of Things (IoT): A survey
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Metadata
- Authors
- Hush, D. R., & Horne, B. G.
- Venue
- IEEE Signal Processing Magazine
- Published
- 1993-01
- Volume
- 10
- Issue
- 1
- Pages
- 8-39
- ISSN
- 1053-5888 (ISSN-L)
- OpenAlex
- W2078376347
- Cited by
- 1,247
- References
- 101
- FWCI
- 79.99
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
