Tunnel Ventilation Control Using Reinforcement Learning Methodology-Reference-Cited by-同舟云学术

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Tunnel Ventilation Control Using Reinforcement Learning Methodology

Published:2006 Issue:4 Volume:49 Page:1003-1010
ISSN:1344-7653
Container-title:JSME International Journal Series C
language:en
Short-container-title:JSME Int. J., Ser. C

Author:

CHU Baeksuk¹,KIM Dongnam¹,HONG Daehie²,PARK Jooyoung³,CHUNG Jin Taek²,KIM Tae-Hyung⁴

Affiliation:

1. Graduate School, Department of Mechanical Engineering, Korea University

2. Department of Mechanical Engineering, Korea University

3. Department of Control and Instrumentation Engineering, Korea University at Jochiwon

4. Fire & Engineering Services Research Department, Korea Institute of Construction Technology

Publisher

Japan Society of Mechanical Engineers

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering

Link

http://www.jstage.jst.go.jp/article/jsmec/49/4/49_4_1003/_pdf

Reference15 articles.

1. Application of fuzzy control to a road tunnel ventilation system

2. (3) Funabashi, M., Aoki, I., Yahiro, M. and Inoue, H., A Fuzzy Model Based Control Scheme and Its Application to a Road Tunnel Ventilation System, Proceedings of IECON ’91, Vol.2 (1991), pp.1596-1601.

3. The control of highway tunnel ventilation using fuzzy logic

4. (5) Chu, B., Kim, D., Hong, D., Park, J., Chung, J.T. and Kim, T.-H., GA-Based Fuzzy Controller Design for Tunnel Ventilation Systems, 22nd International Symposium on Automation and Robotics in Construction, (2005).

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Tunnel ventilation controller design using an RLS-based natural actor-critic algorithm;International Journal of Precision Engineering and Manufacturing;2010-12

2. Model predictive and fuzzy control of a road tunnel ventilation system;Transportation Research Part C: Emerging Technologies;2008-10

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