Common nature of learning between back-propagation and Hopfield-type neural networks for generalized matrix inversion with simplified models.
- DOI
- 10.1109/tnnls.2013.2238555
- Published
- 2013 Apr
- Container
- IEEE transactions on neural networks and learning systems
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tnnls.2013.2238555,
title = {Common nature of learning between back-propagation and Hopfield-type neural networks for generalized matrix inversion with simplified models.},
author = {Zhang Y and Guo D and Li Z},
year = {2013},
journal = {IEEE transactions on neural networks and learning systems},
doi = {10.1109/tnnls.2013.2238555},
url = {https://doi.org/10.1109/tnnls.2013.2238555}
}RIS
TY - JOUR TI - Common nature of learning between back-propagation and Hopfield-type neural networks for generalized matrix inversion with simplified models. AU - Zhang Y AU - Guo D AU - Li Z PY - 2013 JO - IEEE transactions on neural networks and learning systems DO - 10.1109/tnnls.2013.2238555 UR - https://doi.org/10.1109/tnnls.2013.2238555 ER -
APA
Y, Z., D, G., & Z, L. (2013). Common nature of learning between back-propagation and Hopfield-type neural networks for generalized matrix inversion with simplified models.. IEEE transactions on neural networks and learning systems. https://doi.org/10.1109/tnnls.2013.2238555
Source records
- pubmed · retrieved 2026-11-10T14:53:22.786Z