Academic Journal of Computing & Information Science, 2022, 5(13); doi: 10.25236/AJCIS.2022.051312.
Yang Zhou1, Ze Liu2, Xiaoyun Li3
1School of Safety Science and Engineering, Anhui University of Science and Technology, Huainan, 232001, China
2School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, 232001, China
3School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan, 232001, China
The function of the fire detector is to capture specific fire signals. The sensitivity of the detector determines the sensitivity of the response to the characteristics of the fire severity. The sensitivity and reliability of the detector become the key parameters that the detector needs to consider in balance. Aiming at the problem of the most reliable detector types, we first use MATLAB to calculate the reliability and failure rate of the detectors according to the index weights. Then, the SPSSPRO evaluation TOPSIS method is used to score and rank the detectors, and in descending order, the detector with the highest ranking is selected as the detector with the highest reliability. In response to the problem of alarm accuracy, we first preprocess the data, and find suitable indicators according to the requirements of the topic to establish an intelligent judgment model for the type of regional alarm components. The indicators we find here include the name of the component, the name of the project, the affiliation of the fire department, and the probability of fire occurrence, which can be considered, six indicators of the probability of real alarm, and then uses the principal component analysis method to determine the weight through dimension reduction, and further screens the data, and then predicts the indicators through the method of neural network analysis to determine the alarm accuracy rate.
Enumeration method, TOPSIS method, principal component analysis, neural network analysis, fuzzy comprehensive evaluation, gray correlation
Yang Zhou, Ze Liu, Xiaoyun Li. Safety prediction model of fire detection and alarm system based on optimized neural network algorithm. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 13: 77-81. https://doi.org/10.25236/AJCIS.2022.051312.
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