|本期目录/Table of Contents|

[1]袁朋伟,宋守信,董晓庆.基于灰色神经网络优化组合模型的火灾预测研究[J].中国安全生产科学技术,2014,10(3):119-124.[doi:10.11731/j.issn.1673-193x.2014.03.020]
　YUAN Peng wei,SONG Shou xin,DONG Xiao qing.Study on fire accident prediction based on optimized grey neural network combination model[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(3):119-124.[doi:10.11731/j.issn.1673-193x.2014.03.020]
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基于灰色神经网络优化组合模型的火灾预测研究
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
10
期数:
2014年3期
页码:
119-124
栏目:
职业安全卫生管理与技术
出版日期:
2014-03-28

文章信息/Info

Title:
Study on fire accident prediction based on optimized grey neural network combination model
作者:
袁朋伟; 宋守信; 董晓庆
（北京交通大学 经济管理学院，北京100044）
Author(s):
YUAN Pengwei;  SONG Shouxin;  DONG Xiaoqing
（School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China）
关键词:
火灾事故; 火灾事故预测; 灰色系统; 人工神经网络; 遗传算法; 组合预测
Keywords:
fire accident;  fire accident prediction;  grey system;  artificial neural network;  genetic algorithm;  combination forecasting
分类号:
X928.7
DOI:
10.11731/j.issn.1673-193x.2014.03.020
文献标志码:
A
摘要:
为了提高火灾事故预测的精度，根据我国火灾事故数据样本较小，波动性较大的特点，将遗传算法优化的灰色无偏预测模型与遗传算法优化的BP神经网络模型结合起来，建立灰色神经网络优化组合模型，充分发挥无偏灰色预测模型适用于小样本的数据预测的优势与BP神经网络处理非线性问题的优点。分别采用遗传算法优化后的无偏灰色GM（1,1）模型、遗传算法优化的BP神经网络预测模型与灰色神经网络优化组合模型对我国1998-2008年的火灾事故进行拟合，并对2009-2011年的火灾事故发生数进行预测。结果表明：灰色神经网络优化组合模型的预测误差最小，精度最高，适用于火灾事故的预测。
Abstract:
The prediction of fire accident is the basis for fire department planning and decisionmaking. The statistical data of fire accidents in China has the characteristics of small sample and big fluctuation. In order to improve the accuracy of fire accidents prediction, the model combining unbiased grey forecasting model and BP neural network method optimized by genetic algorithm was developed to adapt to the characteristics of fire accident statistical data. The model gave full play to the advantage of unbiased gray prediction model in fitting the small sample and superiority of BP neural network time series prediction for dealing with the nonlinear problems. According to the statistical data of fire accident in 1998-2011, the optimized unbiased GM(1,1)prediction model, the optimized BP neural network time series prediction model and the optimized grey neural network combination model programed by Matlab were used to fit the number of fire accidents, and the 2012-2015 fire accident numbers were predicted. The results showed that the new model has fewer errors and better forecasting precision. Consequently, comparing with the traditional method, the new model is more applicable for the prediction of fire accidents.

参考文献/References:

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备注/Memo

备注/Memo:
2012年北京市哲学社会科学规划项目（12JGB022）；2012年中央高校基本科研业务费项目（2012JBM132）
更新日期/Last Update: 2014-03-30