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Dynamical system parameter identification using deep recurrent cell networks: Which gated recurrent unit and when?

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dc.contributor.author Akagündüz, Erdem
dc.contributor.author Çifdalöz, Oğuzhan
dc.date.accessioned 2023-02-15T11:13:35Z
dc.date.available 2023-02-15T11:13:35Z
dc.date.issued 2021-12
dc.identifier.citation Akagündüz, Erdem; Çifdalöz, Oğuzhan (2021). "Dynamical system parameter identification using deep recurrent cell networks: Which gated recurrent unit and when?", Neural Computing and Applications, Vol. 33, No. 23, pp. 16745-16757. tr_TR
dc.identifier.issn 0941-0643
dc.identifier.uri http://hdl.handle.net/20.500.12416/6246
dc.description.abstract In this paper, we investigate the parameter identification problem in dynamical systems through a deep learning approach. Focusing mainly on second-order, linear time-invariant dynamical systems, the topic of damping factor identification is studied. By utilizing a six-layer deep neural network with different recurrent cells, namely GRUs, LSTMs or BiLSTMs; and by feeding input/output sequence pairs captured from a dynamical system simulator, we search for an effective deep recurrent architecture in order to resolve the damping factor identification problem. Our study’s results show that, although previously not utilized for this task in the literature, bidirectional gated recurrent cells (BiLSTMs) provide better parameter identification results when compared to unidirectional gated recurrent memory cells such as GRUs and LSTM. Thus, indicating that an input/output sequence pair of finite length, collected from a dynamical system and when observed anachronistically, may carry information in both time directions to predict a dynamical systems parameter. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1007/s00521-021-06271-5 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject BiLSTM tr_TR
dc.subject Dynamical Systems Parameter Identification tr_TR
dc.subject GRU tr_TR
dc.subject LSTM tr_TR
dc.subject Recurrent Cells tr_TR
dc.title Dynamical system parameter identification using deep recurrent cell networks: Which gated recurrent unit and when? tr_TR
dc.type article tr_TR
dc.relation.journal Neural Computing and Applications tr_TR
dc.contributor.authorID 279762 tr_TR
dc.identifier.volume 33 tr_TR
dc.identifier.issue 23 tr_TR
dc.identifier.startpage 16745 tr_TR
dc.identifier.endpage 16757 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü tr_TR


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