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Wang Y, Yu CY, Tu R, Zhang YM (2011) Expert Systems with Applications 38(8), 9580-9586.
Date: 2011-08-16   Author: SKLFS   Source: WOS
 

Wang Y, Yu CY, Tu R, Zhang YM (2011) Fire detection model in Tibet based on grey-fuzzy neural network algorithm. Expert Systems with Applications 38(8), 9580-9586. [In English]

Web link: http://dx.doi.org/10.1016/j.eswa.2011.01.163

Keywords:

Fire detection; ANFIS; GM(1,1); Tibet; compartment fire; system; anfis; aircraft; smoke

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. (C) 2011 Elsevier Ltd. All rights reserved.

 
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Relevant link
Wu LB, Chen T, Ma SH (2002) (Science Press Beijing: Beijing) 397-402
Yu CY, Fang J, Wang JJ, Zhang YM (2010) Fire Technology 46(3), 651-663.
Fang J, Yu CY, Qiao LF, Zhang YM, Wang JJ (2008) (Science Press Beijing: Beijing) 731-734
Jun F, Yu CY, Ran T, Qiao LF, Zhang YM, Wang JJ (2008) Journal of Hazardous Materials 154(1-3), 476-483.
Research progress on fire prevention technology in the low-altitude low-oxygen plateau environment
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