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dc.contributor.authorErgen, Tolgaen_US
dc.contributor.authorKozat, Süleyman Serdaren_US
dc.coverage.spatialAntalya, Turkeyen_US
dc.date.accessioned2018-04-12T11:45:12Z
dc.date.available2018-04-12T11:45:12Z
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/11693/37601
dc.descriptionDate of Conference: 15-18 May 2017en_US
dc.descriptionConference Name: IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017en_US
dc.description.abstractIn this paper, we investigate online nonlinear regression and introduce novel algorithms based on the long short term memory (LSTM) networks. We first put the underlying architecture in a nonlinear state space form and introduce highly efficient particle filtering (PF) based updates, as well as, extended Kalman filter (EKF) based updates. Our PF based training method guarantees convergence to the optimal parameter estimation under certain assumptions. We achieve this performance with a computational complexity in the order of the first order gradient based methods by controlling the number of particles. The experimental results illustrate significant performance improvements achieved by the introduced algorithms with respect to the conventional methods.en_US
dc.language.isoTurkishen_US
dc.source.titleProceedings of the IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/SIU.2017.7960218en_US
dc.subjectKalman filteringen_US
dc.subjectLong short term memory networken_US
dc.subjectOnline learningen_US
dc.subjectParticle filteringen_US
dc.subjectComplex networksen_US
dc.subjectSignal filtering and predictionen_US
dc.subjectState space methodsen_US
dc.subjectGradient-based methoden_US
dc.subjectNon-linear regressionen_US
dc.subjectOptimal parameter estimationen_US
dc.subjectShort term memoryen_US
dc.titleNeural networks based online learningen_US
dc.title.alternativeSinir ağları merkezli çevrimiçi öğrenimen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.identifier.doi10.1109/SIU.2017.7960218en_US
dc.publisherIEEEen_US


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