DC Field | Value | Language |
---|---|---|
dc.contributor.author | Todorovic, Branimir | en |
dc.contributor.author | Stanković, Miomir | en |
dc.contributor.author | Moraga, Claudio | en |
dc.date.accessioned | 2020-12-11T13:04:36Z | - |
dc.date.available | 2020-12-11T13:04:36Z | - |
dc.date.issued | 2006-12-01 | en |
dc.identifier.isbn | 1-4244-0433-9 | en |
dc.identifier.uri | http://researchrepository.mi.sanu.ac.rs/handle/123456789/4381 | - |
dc.description.abstract | We consider the problem of recurrent neural network training as a Bayesian state estimation. The proposed algorithm uses Gaussian sum filter for nonlinear, non-Gaussian estimation of network outputs and synaptic weights. The performances of the proposed algorithm and other Bayesian filters are compared in noisy chaotic time series long-term prediction. | en |
dc.publisher | IEEE | - |
dc.relation.ispartof | 8th Seminar on Neural Network Applications in Electrical Engineering, Neurel-2006 Proceedings | en |
dc.subject | Divided difference filter | Extended Kalman filter | Gaussian sum filter | Recurrent neural networks | Sequential Bayesian estimation | Unscented kalman filter | en |
dc.title | Gaussian sum filters for recurrent neural networks training | en |
dc.type | Conference Paper | en |
dc.identifier.doi | 10.1109/NEUREL.2006.341175 | en |
dc.identifier.scopus | 2-s2.0-46749152690 | en |
dc.relation.firstpage | 53 | en |
dc.relation.lastpage | 57 | en |
item.grantfulltext | none | - |
item.cerifentitytype | Publications | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | Conference Paper | - |
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