Design of Fire Warning System Based on Multilayer Fuzzy Neural Network
Aiming at the problem that the urban fire warning capability needs to be improved,the data composition characteristics of urban fire remote monitoring system are analyzed.The logical architecture of data warning focusing on the mining of fire inspection radio frequency punch card data and temperature data is described.Polynomial and logarithmic depth iterative regression fuzzy algorithms are used to design local two-column data with different time value domains by means of fuzzy matrix method for generating fuzzy warning after importing the output values into the warning result collation module.At the same time,the current time point is constrained,and the reference matrix formed by external city-wide data from other nodes is used to construct the simulation design scheme.Through the simulation test with real data,it is verified that the sensitivity and specificity of the system under different fire warning levels meet the requirements.Compared with other machine learning algorithms in the available references reveals,the fire warning system optimal values of the system are better.The system possesses a credible statistical advantage.
Fire warningRemote monitoring systemFuzzy algorithmNeural networkDeep iterative regressionSensitivity
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