dc.creator | Perez Bello, Dinibel | |
dc.creator | Natali, Maria Paula | |
dc.creator | Meza, Amalia Margarita | |
dc.date.accessioned | 2021-03-26T10:47:30Z | |
dc.date.accessioned | 2022-10-15T09:29:52Z | |
dc.date.available | 2021-03-26T10:47:30Z | |
dc.date.available | 2022-10-15T09:29:52Z | |
dc.date.created | 2021-03-26T10:47:30Z | |
dc.date.issued | 2019-12 | |
dc.identifier | Perez Bello, Dinibel; Natali, Maria Paula; Meza, Amalia Margarita; Comparison of adaptive neuro-fuzzy inference system and recurrent neural network in vertical total electron content forecasting; Springer; Neural Computing And Applications; 31; 12; 12-2019; 8411-8422 | |
dc.identifier | 0941-0643 | |
dc.identifier | http://hdl.handle.net/11336/129012 | |
dc.identifier | CONICET Digital | |
dc.identifier | CONICET | |
dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/4370550 | |
dc.description.abstract | Accurate prediction of total electron content (TEC) is important for monitoring the behavior of the ionosphere and indeed a magnitude of interest to understand the properties and behavior of the Sun–Earth System. The conditions of this medium have a direct impact on a growing variety of critical technological infrastructure. This work presents a comparison between two different artificial neural networks (ANNs): an adaptive neuro-fuzzy inference system and nonlinear autoregressive neural network (NAR-NN) applied to TEC. Both ANNs where tested on four different geomagnetic locations on 4 1-week periods having a variety of geomagnetic disturbance levels. The effect of using different training period lengths and the system response for 60 and 30 min sampling rate TEC time series was investigated. NAR-NN shows a slightly better performance, being the higher difference during the greater perturbations. There is also a better response when sampling rates of 30 min are used. | |
dc.language | eng | |
dc.publisher | Springer | |
dc.relation | info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s00521-019-04528-8 | |
dc.relation | info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1007/s00521-019-04528-8 | |
dc.rights | https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ | |
dc.rights | info:eu-repo/semantics/restrictedAccess | |
dc.subject | FORECASTING | |
dc.subject | NEURAL NETWORK | |
dc.subject | SPACE WEATHER | |
dc.subject | VTEC | |
dc.title | Comparison of adaptive neuro-fuzzy inference system and recurrent neural network in vertical total electron content forecasting | |
dc.type | info:eu-repo/semantics/article | |
dc.type | info:ar-repo/semantics/artículo | |
dc.type | info:eu-repo/semantics/publishedVersion | |