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Learning system dynamics via deep recurrent and conditional neural systems

Title	Learning system dynamics via deep recurrent and conditional neural systems
Author	Pekmezci, Mehmet, Uğur, E., Öztop, Erhan
Publication Date:	2021
Publication Place	- IEEE
Subject	CNMP, Deep learning, LSTM, System dynamics
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-166543649-6
Record ID	a412db35-0581-4289-8ead-a688cfceb88d
Library Location	Computer Science
Date	2021
Sample Text	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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Özyeğin University

Learning system dynamics via deep recurrent and conditional neural systems

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

Publication Date 2021

Publication Place - IEEE

Subject CNMP, Deep learning, LSTM, System dynamics

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-166543649-6

Record ID a412db35-0581-4289-8ead-a688cfceb88d

Library Location Computer Science

Date 2021

Sample Text 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