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关于重庆儿童医院1998年至2007年传染病住院构成及变迁调查
时间:2011-01-23 浏览次数:1032次 无忧论文网
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【中文摘要】 目的:1.用重庆市渝中区2003-2005年水质信息数据作为研究对象。选择高锰酸盐指数、粪大肠菌群数作为水质参数的代表。引入支持向量回归机对高锰酸盐指数、粪大肠菌群数月平均浓度进行统计预测。并将该模型的预测性能与BP神经网络、RBF神经网络模型进行比较。2.用重庆市渝中区2003-2005年各医院统计的介水性传染病住院人数为研究对象,引入支持向量回归机建立水质对人群健康影响的预测模型。并将该模型的预测性能与BP神经网络、RBF神经网络模型进行比较。3.为水资源管理和污染控制以及介水性传染病的预测和控制提供方法学参考。方法:分别采用支持向量回归机和人工神经网络对重庆市渝中区2003-2005年的水质(粪大肠菌群月平均浓度、高锰酸盐指数月平均浓度)以及每月因患介水性传染病而住院的人数进行预测,并采用有关统计预测指标对预测效果进行比较评价。结果:采用均方百分比误差(RMSE)和平均绝对百分比误差(MAPE)来评价模型预测效果。实例表明,对于水质以及介水传染病住院人数预测,支持向量机回归机模型预测精度均高于BP神经网络与RBF神经网络。结论:1、与基于经验风险最小化准则的人工神经网络比较,基于...更多结构风险最小化准则的支持向量回归机更适合于有限样本的研究,并且可以减少过拟合,因而有较好的泛化能力,更适合于本资料的预测。2、本研究为水资源污染控制及介水性传染病的预防和控制在方法学上进行了一次有益探索。 

【英文摘要】 Objectives:1. A support vector regression machine based forecasting model was established for predicting the monthly average concentration of permanganate index and aecal coliforms of water quality, with the water quality information from 2003 to 2005 in Yuzhong District of Chongqing. The forecasting performance were compared with BP Neutral Network (BPNN)and radial basis function neural network(RBFNN).2. A support vector regression machine based forecasting model was established for predicting the influence of the water quality on population health, based on the number of water-based infectious diseases inpatients from 2003 to 2005 in Yuzhong District of Chongqing. The forecasting performance were compared with BP Neutral Network (BPNN) and radial basis function Neural Network (RBFNN).3. These results can provide reference to water management, pollution control and the water-based infectious diseases prediction and control.Methods:SVR,BPNN and RBFNN were used to forecast the water qua...更多lity and the number of water-based infectious diseases inpatients from 2003 to 2005 in Yuzhong District of Chongqing. Prediction results were compared by some statistical indexes.Results:RMSE and MAPE were used to evaluate the forecasting results. It indicates that the precision of support vector regression machine is superior to BP Neutral Network and RBF Neural Network.Conclusions:1. On study of small-sample data, SVM based on structural risk minimization principle has better generalization performance than BPNN and RBFNN which based on empirical risk minimization principle. 2. It is a practical and beneficial exploration in the water pollution control and the water-based infectious diseases prediction and control. 

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