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Study on Fire Detection Model Based on Fuzzy Neural Network

机译:基于模糊神经网络的火灾检测模型研究

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The fire signal detection is a non-structural problem and difficult to be precise described by mathematical model, which increase the difficulty of fire detection. According to the special type of signal detection technique such as fire signal detection, a fire detection model based on fuzzy-neural network is presented. This paper described the design method of the model, as well as its learning algorithm. In standard fire test rooms, simulation experiments were carried out for smoldering fire SH_1 and flaming fire SH_3 of the china national standard test fires, the model can make right judgment Theory analysis and simulation study show that the model combines the advantages of fuzzy system and neural network, and improves the intelligence of fire detection, has a stronger ability to adapt the environment. It effectively solves the problems of mistake and failure in the fire alarm, and improves the sensibility of fire detection.
机译:火信号检测是非结构问题,数学模型难以精确描述,这增加了火灾探测的难度。 根据诸如火星检测的特殊类型的信号检测技术,介绍了基于模糊神经网络的火灾检测模型。 本文描述了模型的设计方法,以及其学习算法。 在标准的消防车中,进行了仿真实验,进行了闷烧的火灾SH_1和中国国家标准测试火灾的火焰火灾SH_3,该模型可以做出正确的判断理论分析和仿真研究表明,该模型结合了模糊系统和神经的优势 网络,并提高火灾侦测的智能,具有更强的适应环境的能力。 它有效解决了火灾报警器中的错误和失效问题,并提高了火灾探测的敏感性。

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