{"title":"基于反向传播神经网络的智能火灾识别系统研究","authors":"Shaopeng Yu, Liyuan Dong, Fengyuan Pang","doi":"10.1142/s1469026823500141","DOIUrl":null,"url":null,"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.","PeriodicalId":45994,"journal":{"name":"International Journal of Computational Intelligence and Applications","volume":" ","pages":""},"PeriodicalIF":0.8000,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Study of Intelligent Fire Identification System Based on Back Propagation Neural Network\",\"authors\":\"Shaopeng Yu, Liyuan Dong, Fengyuan Pang\",\"doi\":\"10.1142/s1469026823500141\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":45994,\"journal\":{\"name\":\"International Journal of Computational Intelligence and Applications\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2023-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Computational Intelligence and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1142/s1469026823500141\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computational Intelligence and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/s1469026823500141","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Study of Intelligent Fire Identification System Based on Back Propagation Neural Network
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.
期刊介绍:
The International Journal of Computational Intelligence and Applications, IJCIA, is a refereed journal dedicated to the theory and applications of computational intelligence (artificial neural networks, fuzzy systems, evolutionary computation and hybrid systems). The main goal of this journal is to provide the scientific community and industry with a vehicle whereby ideas using two or more conventional and computational intelligence based techniques could be discussed. The IJCIA welcomes original works in areas such as neural networks, fuzzy logic, evolutionary computation, pattern recognition, hybrid intelligent systems, symbolic machine learning, statistical models, image/audio/video compression and retrieval.