Abstract
In order to detect and identify fire accidents accurately and efficiently, an intelligent fire identification system based on neural network algorithm is designed, which can overcome the shortcomings of single information, complex wiring, poor adaptability, etc. The characteristic extraction of sensors is adopted in the information layer to solve the problems in multi-sensor fusion. The fire data are transmitted to the main controller through LoRa wireless module and fused by back propagation neural network, which is self-learning and adaptive. The output of neural network and fuzzy inference with other factors are used for decision criteria to improve the identification accuracy. The common combustibles and various interference sources are selected for fire tests. The result shows that the detection accuracy is up to 100% and the false alarm rate is lower than 0.1%, meanwhile, the system has the advantages of fast response and high detection efficiency.
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