基于反向传播神经网络的智能火灾识别系统研究

IF 0.8 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Shaopeng Yu, Liyuan Dong, Fengyuan Pang
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引用次数: 0

摘要

为了准确高效地检测和识别火灾事故,设计了一种基于神经网络算法的智能火灾识别系统,该系统可以克服信息单一、布线复杂、适应性差等缺点。在信息层采用传感器的特征提取来解决多传感器融合中的问题。火灾数据通过LoRa无线模块传输到主控制器,并通过自学习和自适应的反向传播神经网络进行融合。将神经网络的输出和其他因素的模糊推理用于决策准则,以提高识别精度。火灾试验选用了常见的可燃物和各种干扰源。结果表明,该系统的检测准确率高达100%,误报率低于0.1%,同时具有响应快、检测效率高的优点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
CiteScore
2.90
自引率
0.00%
发文量
25
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.
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