论文部分内容阅读
为提高建筑施工事故灰色预测模型精度,在传统GM(1,1)模型基础上,建立非线性灰色伯努利模型(NGBM),并采用粒子群优化(PSO)算法对参数进行优选。以2001—2011年全国建筑事故死亡人数统计数据为基础,运用该模型对2012—2013年的相应人数进行预测,并与GM(1,1)模型和灰色Verhulst模型的结果相对比。结果表明,NGBM拟合精度最好,平均相对误差仅为2.65%,验证了模型的可行性和准确性。
In order to improve the precision of gray prediction model of construction accidents, a nonlinear gray Bernoulli model (NGBM) is established based on the traditional GM (1,1) model, and a particle swarm optimization (PSO) algorithm is adopted to optimize the parameters. Based on the statistical data of death toll from construction accidents in China during 2001-2011, this model is used to predict the corresponding numbers in 2012-2013, which are compared with the results of GM (1,1) model and gray Verhulst model. The results show that NGBM has the best fitting accuracy and the average relative error is only 2.65%, which verifies the feasibility and accuracy of the model.