基于BP神经网络的消防管理系统态势预测

Yunyang
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引用次数: 1

摘要

本文介绍了BP神经网络实现训练算法的原理,该算法结合Hopfield神经网络联想记忆实现对社区半封闭空间火灾的预测。将BP神经网络看作是从输入到输出的非线性映射。基于BP神经网络算法,通过软件监测技术获得预测模型,预测的输出值与实际值接近,通过具体现场仿真的对比验证了人工神经网络的高效性。一旦发生火灾,人们可以通过烟雾和火焰的颜色及时获取灭火材料,防止火势扩大。因此,可以通过火灾模型的模式来预测和预防火灾。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Situation Prediction of Fire Management System Based on BP Neural Network
This paper introduces the principle of BP Neural Network to achieve Training algorithm, which work with Hopfield neural network associative memory to achieve prediction fire in the semi-closed space of the community. The BP Neural Network is regarded as a nonlinear mapping from input to output. Based on the BP neural network algorithm by the software monitoring technology obtain the prediction model which predict the output value is closed to the real value The high effectiveness of Artificial Neural Network is verified by the comparison of specific field simulation. Once the fire happened, People can get fire of extinguishing materials in time by the color of the smoke and fire emitted to prevent the expansion of the fire. Consequently, the fire disasters can be predicted and prevented through the pattern of the fire model.
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