Fire detection model in Tibet based on grey-fuzzy neural network algorithm |
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| Authors: | Yan Wang Chunyu Yu Ran Tu Yongming Zhang |
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| Affiliation: | 1. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Zhongguancun East Road No.55, Beijing, 100190, China;2. Regional Economics Applications Laboratory, University of Illinois, 607 S. Mathews, #318, Urbana, IL 61801, USA;1. Department of Physics, Covenant University Canaan land, P.M.B 1023, Ota, Nigeria;2. Department of Mathematics, Federal University of Technology, Minna, Nigeria;3. Department of Mechanical Engineering and Science, University of Johannesburg, APK, South Africa;1. College of Management Engineering and Business, Hebei University of Engineering, Handan, 056038, China;2. School of Management, Harbin Institute of Technology, Harbin, 150001, China;1. School of Economics, Zhejiang University of Finance and Economics, Hangzhou, 310018, China;2. School of Economics and Management, Beihang University, Beijing, 100191, China;1. School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China;2. School of Chemical Engineering, The University of Queensland, St Lucia, QLD 4072, Australia |
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| Abstract: | The fire signals are much weaker in low oxygen concentration and low pressure environment such as Tibet. Fire detectors which were calibrated in correlating standard conditions cannot work well in such condition. This paper presents a synthesis method of GM(1, 1) grey prediction model and adaptive neuro-fuzzy inference system (ANFIS) in advance to detect fire and to make it work in the environment. The theoretical analysis of the algorithm and experimental evaluation in Tibet are presented. In this process, the grey GM(1, 1) predict model can anticipate the development of fire signals without any assumption, thus allowing earlier fire alarm than traditional fire detection equipments, meanwhile, ANFIS can make sure the data processing more accurate to avoid false alarms. This work will supply useful suggestions with the fire detectors design in low ambient pressure and low oxygen concentration such as Tibet, etc. |
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