Adaptive neuro-fuzzy interface system (ZNFIS) controller for polymerization reactor

Hosen, Mohammad, Nahavandi, Saeid, Sinnott, Lachlan and Khosravi, Abbas 2016, Adaptive neuro-fuzzy interface system (ZNFIS) controller for polymerization reactor, in 21CW 2016 : Proceedings of the IEEE Conference on Norbert Wiener in the 21st Century, IEEE, Piscataway, N.J., pp. 34-39, doi: 10.1109/NORBERT.2016.7547456.

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Title Adaptive neuro-fuzzy interface system (ZNFIS) controller for polymerization reactor
Author(s) Hosen, MohammadORCID iD for Hosen, Mohammad
Nahavandi, SaeidORCID iD for Nahavandi, Saeid
Sinnott, Lachlan
Khosravi, AbbasORCID iD for Khosravi, Abbas
Conference name Norbert Wiener in the 21st Century. Conference (2016 : Melbourne, Vic.)
Conference location Melbourne, Vic.
Conference dates 13-15 Jul. 2016
Title of proceedings 21CW 2016 : Proceedings of the IEEE Conference on Norbert Wiener in the 21st Century
Publication date 2016
Start page 34
End page 39
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Science & Technology
Computer Science, Artificial Intelligence
Computer Science, Interdisciplinary Applications
Engineering, Electrical & Electronic
Computer Science
Neural network
Nonlinear controller
Summary It is a challenging task to control polymerization reactor due to the complex reactions mechanism. Moreover, the dynamic behaviour of the polymerization reactor is highly nonlinear. Thousand of reactions involed during polymerization that make the system complex in nature. Artificial intelligent appeared as promising tool to control such kind of nonlinear and complex processes. In the present work, a advanced nonlinear controller, namely adaptive neuro-fuzzy interface system (ANFIS) is proposed and designed for polymerization reactor. Sugeno type fuzzy interface system is used in ANFIS. Hybrid optimization algorithm, a combination of least-square estimation and backpropagation methods is used to optimize the neural network-based fuzzy output model. Styrene free radical polymerisation batch reactor is used as a case study. Simulation results demonstrated that the tracking performance of the ANFIS-based controller is better than the traditional neural network (NN)-based controller.
ISBN 9781467383806
Language eng
DOI 10.1109/NORBERT.2016.7547456
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 0 Not Applicable
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, IEEE
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