Proceedings of the 2017 2nd International Conference on Automation, Mechanical and Electrical Engineering (AMEE 2017)

Optimization and Simulation of Boiler Water Level Control Based on the Fuzzy Neural Network

Authors
Zhimin Yu, Long Cheng, Zhenyong Hao, Wang Renzhong
Corresponding Author
Zhimin Yu
Available Online September 2017.
DOI
10.2991/amee-17.2017.9How to use a DOI?
Keywords
boiler water level control; fuzzy logic;fuzzy neural network g; PID
Abstract

To adjust the parameter PID of boiler water level control.Analysis of the problems existing in the Marine boiler water level control.[Method] Using fuzzy neural network, Fuzzy neural network is the combination of neural network and fuzzy logic. the traditional PID control and fuzzy neural network control modeling, simulation comparison.[Result] The results show that the fuzzy neural network control convergence is good, the method is effective.[Conclusion] The conclusion is of reference significance to improve boiler water level control.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 2nd International Conference on Automation, Mechanical and Electrical Engineering (AMEE 2017)
Series
Advances in Engineering Research
Publication Date
September 2017
ISBN
978-94-6252-393-7
ISSN
2352-5401
DOI
10.2991/amee-17.2017.9How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Zhimin Yu
AU  - Long Cheng
AU  - Zhenyong Hao
AU  - Wang Renzhong
PY  - 2017/09
DA  - 2017/09
TI  - Optimization and Simulation of Boiler Water Level Control Based on the Fuzzy Neural Network
BT  - Proceedings of the 2017 2nd International Conference on Automation, Mechanical and Electrical Engineering (AMEE 2017)
PB  - Atlantis Press
SP  - 47
EP  - 51
SN  - 2352-5401
UR  - https://doi.org/10.2991/amee-17.2017.9
DO  - 10.2991/amee-17.2017.9
ID  - Yu2017/09
ER  -