Learning system dynamics via deep recurrent and conditional neural systems

Name: Learning system dynamics via deep recurrent and conditional neural systems
Author: Pekmezci, Mehmet, Uğur, E., Öztop, Erhan

İsim	Learning system dynamics via deep recurrent and conditional neural systems
Yazar	Pekmezci, Mehmet, Uğur, E., Öztop, Erhan
Basım Tarihi:	2021
Basım Yeri	- IEEE
Konu	CNMP, Deep learning, LSTM, System dynamics
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-166543649-6
Kayıt Numarası	a412db35-0581-4289-8ead-a688cfceb88d
Lokasyon	Computer Science
Tarih	2021
Örnek Metin	Although there are various mathematical methods for modeling system dynamics, more general solutions can be achieved using deep learning based on data. Alternative deep learning methods are presented in parallel with the improvements in artificial neural networks. In this study, both LSTM-based recurrent deep learning method and CNMP-based conditional deep learning method were used to learn the system dynamics of the selected system using time series data. The effects of the amount of time series data needed for training and the initial input length needed for predictions made using the learned system model on both methods were analyzed.
DOI	10.1109/SIU53274.2021.9478006

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Learning system dynamics via deep recurrent and conditional neural systems

Yazar Pekmezci, Mehmet, Uğur, E., Öztop, Erhan

Basım Tarihi 2021

Basım Yeri - IEEE

Konu CNMP, Deep learning, LSTM, System dynamics

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-166543649-6

Kayıt Numarası a412db35-0581-4289-8ead-a688cfceb88d

Lokasyon Computer Science

Tarih 2021

Örnek Metin Although there are various mathematical methods for modeling system dynamics, more general solutions can be achieved using deep learning based on data. Alternative deep learning methods are presented in parallel with the improvements in artificial neural networks. In this study, both LSTM-based recurrent deep learning method and CNMP-based conditional deep learning method were used to learn the system dynamics of the selected system using time series data. The effects of the amount of time series data needed for training and the initial input length needed for predictions made using the learned system model on both methods were analyzed.

DOI 10.1109/SIU53274.2021.9478006