Article | Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016 | Dynamic Artificial Neural Network (DANN) MATLAB Toolbox for Time Series Analysis and Prediction Linköping University Electronic Press Conference Proceedings
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Title:
Dynamic Artificial Neural Network (DANN) MATLAB Toolbox for Time Series Analysis and Prediction
Author:
Khim Chhantyal: Faculty of Technology, Natural Sciences, and Maritime Sciences, University College of Southeast Norway Minh Hoang: Faculty of Technology, Natural Sciences, and Maritime Sciences, University College of Southeast Norway Håkon Viumdal: Faculty of Technology, Natural Sciences, and Maritime Sciences, University College of Southeast Norway Saba Mylvaganam: Faculty of Technology, Natural Sciences, and Maritime Sciences, University College of Southeast Norway
DOI:
10.3384/ecp17142568
Download:
Full text (pdf)
Year:
2018
Conference:
Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016
Issue:
142
Article no.:
083
Pages:
568-574
No. of pages:
7
Publication type:
Abstract and Fulltext
Published:
2018-12-19
ISBN:
978-91-7685-399-3
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


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MATLAB® Neural Network (NN) Toolbox can handle both static and dynamic neural networks. Using MATLAB® NN Toolbox with recurrent neural networks is not straight forward. We present a Dynamic Arti?cial Neural Network (DANN) MATLAB toolbox capable of handling fully connected neural networks for time-series analysis and predictions. Three different learning algorithms are incorporated in the MATLAB DANN toolbox: Back Propagation Through Time (BPTT) an of?ine learning algorithm and two online learning algorithms; Real Time Recurrent Learning (RTRL) and Extended Kalman Filter (EKF). In contrast to existing MATLAB® NN Toolbox, the presented MATLAB DANN toolbox has a possibility to perform the optimal tuning of network parameters using grid search method. Three different cases are used for testing three different learning algorithms. The simulation studies con?rm that the developed MATLAB DANN toolbox can be easily used in time-series prediction applications successfully. Some of the essential features of the learning algorithms are seen in the graphical user interfaces discussed in the paper. In addition, installation guide for the MATLAB DANN toolbox is also given.

Keywords: dynamic arti?cial neural network (DANN), back propagation through time (BPTT), real-time recurrent learning (RTRL), extended Kalman filter (EKF), time series

Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016

Author:
Khim Chhantyal, Minh Hoang, Håkon Viumdal, Saba Mylvaganam
Title:
Dynamic Artificial Neural Network (DANN) MATLAB Toolbox for Time Series Analysis and Prediction
DOI:
http://dx.doi.org/10.3384/ecp17142568
References:
No references available

Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016

Author:
Khim Chhantyal, Minh Hoang, Håkon Viumdal, Saba Mylvaganam
Title:
Dynamic Artificial Neural Network (DANN) MATLAB Toolbox for Time Series Analysis and Prediction
DOI:
https://doi.org10.3384/ecp17142568
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Last updated: 2019-10-02