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Prediction interval-based neural network controller for nonlinear processes

Hosen, Mohammad, Khosravi, Abbas, Nahavandi, Saeid, Creighton, Douglas and Salaken, Syed Moshfeq 2015, Prediction interval-based neural network controller for nonlinear processes, in IJCNN 2015 : Proceedings of the International Joint Conference on Neural Networks, IEEE, Piscataway, N.J., pp. 1-6, doi: 10.1109/IJCNN.2015.7280382.

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Title Prediction interval-based neural network controller for nonlinear processes
Author(s) Hosen, MohammadORCID iD for Hosen, Mohammad orcid.org/0000-0001-8282-3198
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Nahavandi, Saeid
Creighton, DouglasORCID iD for Creighton, Douglas orcid.org/0000-0002-9217-1231
Salaken, Syed Moshfeq
Conference name Neural Networks. International Joint Conference (2015 : Killarney, Ireland)
Conference location Killarney, Ireland
Conference dates 12-17 Jul. 2015
Title of proceedings IJCNN 2015 : Proceedings of the International Joint Conference on Neural Networks
Publication date 2015
Start page 1
End page 6
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) prediction interval
uncertainties and disturbances
nonlinear controllers
PIC
neural networks
Summary Prediction interval (PI) has been extensively used to predict the forecasts for nonlinear systems as PI-based forecast is superior over point-forecast to quantify the uncertainties and disturbances associated with the real processes. In addition, PIs bear more information than point-forecasts, such as forecast accuracy. The aim of this paper is to integrate the concept of informative PIs in the control applications to improve the tracking performance of the nonlinear controllers. In the present work, a PI-based controller (PIC) is proposed to control the nonlinear processes. Neural network (NN) inverse model is used as a controller in the proposed method. Firstly, a PI-based model is developed to construct PIs for every sample or time instance. The PIs are then fed to the NN inverse model along with other effective process inputs and outputs. The PI-based NN inverse model predicts the plant input to get the desired plant output. The performance of the proposed PIC controller is examined for a nonlinear process. Simulation results indicate that the tracking performance of the PIC is highly acceptable and better than the traditional NN inverse model-based controller.
ISBN 9781479919604
Language eng
DOI 10.1109/IJCNN.2015.7280382
Field of Research 090407 Process Control and Simulation
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082485

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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