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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
引用本文: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(3):119-124.
作者姓名:袁朋伟  宋守信  董晓庆
作者单位:北京交通大学经济管理学院,北京100044
基金项目: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(2012JBMl32)
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。

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

Study on fire accident prediction based on optimized grey neural network combination model
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(3):119-124.
Authors:YUAN Peng-wei  SONG Shou-xin  DONG Xiao-qing
Institution:School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)
Abstract:The prediction of fire accident is the basis for fire department planning and decision-making. The statis- tical 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 net- work 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.
Keywords:fire accident  fire accident prediction  grey system  artificial neural network  genetic algorithm  com-bination forecasting
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