Fire detection model in Tibet based on grey-fuzzy neural network algorithm

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.

Research highlights
► Fire detectors which were calibrated in correlating standard conditions can not work well in Tibet because the special environment can lead to the different fire signals.
► Grey-fuzzy neural network was proposed to realize the fire detection in Tibet.
► The grey GM(1, 1) model can predict the development of fire signals without any assumption.
► 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.