| dc.contributor.author | Ruffini, Marco | |
| dc.contributor.author | Zhu, Shengxiang | |
| dc.contributor.author | Gutterman, Craig | |
| dc.contributor.author | Díaz-Montiel, Alan | |
| dc.contributor.author | Yu, Jiakai | |
| dc.contributor.author | Zussman, Gil | |
| dc.contributor.author | Kilper, Daniel | |
| dc.date.accessioned | 2020-02-04T16:27:13Z | |
| dc.date.available | 2020-02-04T16:27:13Z | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020 | en |
| dc.identifier.citation | Zhu, S., Gutterman, C., Díaz-Montiel, A., Yu, J., Ruffini, M., Zussman, G. & Kilper, D., Hybrid Machine Learning EDFA Model, Optical Fibre Communications (OFC), 2020, 1 - 3 | en |
| dc.identifier.other | Y | |
| dc.identifier.uri | http://hdl.handle.net/2262/91432 | |
| dc.description | PUBLISHED | en |
| dc.description.abstract | A hybrid machine learning (HML) model combining a-priori and a-posteriori knowledge
is implemented and tested, which is shown to reduce the prediction error and training complexity,
compared to an analytical or neural network learning model. | en |
| dc.format.extent | 1 | en |
| dc.format.extent | 3 | en |
| dc.language.iso | en | en |
| dc.rights | Y | en |
| dc.subject | Hybrid machine learning | en |
| dc.subject | 5G networks | en |
| dc.subject | Data centre interconnection networks | en |
| dc.subject | Ebrium doped fiber amplifiers | en |
| dc.title | Hybrid Machine Learning EDFA Model | en |
| dc.title.alternative | Optical Fibre Communications (OFC) | en |
| dc.type | Conference Paper | en |
| dc.type.supercollection | scholarly_publications | en |
| dc.type.supercollection | refereed_publications | en |
| dc.identifier.peoplefinderurl | http://people.tcd.ie/ruffinm | |
| dc.identifier.rssinternalid | 211410 | |
| dc.rights.ecaccessrights | openAccess | |
| dc.identifier.orcid_id | 0000-0001-6220-0065 | |
| dc.rights.restrictedAccess | Y | |
| dc.date.restrictedAccessEndDate | 2020-03-15 | |
