CAJ | 학술논문

目的探讨BP神经网络预测模型在分析气象因素与脑出血死亡率关系中的应用.方法根据BP神经网络的特性,利用MATLAB 7.0软件的神经网络工具箱对2007-2009年哈尔滨市气象数据建立脑出血死亡率的BP神经网络预报模型,并与传统的多元线性回归模型进行比较.结果利用多元线性回归结果显示脑出血死亡率与最高气温、最小相对湿度呈负相关,与平均相对湿度、日照时数呈正相关.脑出血死亡率的非线性相关系数(RNL)为0.7854,平均绝对误差百分比( MAPE)为0.21,均方误差(MSE)为0 22,平均绝对识差(MAE)为0.19,预测准确度(P)为81.31％,平均误差率为0.19.BP神经网络模型的拟合结果显示,脑出血死亡率的RNL为0 7967,MAPE 为0.19,MSE为0.21,MAE为0.18,P为82.53％,平均误差率为0.17.结论应用BP神经网络预测模型对2010年哈尔滨市脑出血死亡率进行预报,通过与多元线性回归模型预报结果进行比较,表明该模型具有更高的预报准确度.
목적 탐토BP신경망락예측모형재분석기상인소여뇌출혈사망솔관계중적응용.방법 근거BP신경망락적특성,이용MATLAB 7.0연건적신경망락공구상대2007-2009년합이빈시기상수거건립뇌출혈사망솔적BP신경망락예보모형,병여전통적다원선성회귀모형진행비교.결과 이용다원선성회귀결과현시뇌출혈사망솔여최고기온、최소상대습도정부상관,여평균상대습도、일조시수정정상관.뇌출혈사망솔적비선성상관계수(RNL)위0.7854,평균절대오차백분비( MAPE)위0.21,균방오차(MSE)위0 22,평균절대식차(MAE)위0.19,예측준학도(P)위81.31％,평균오차솔위0.19.BP신경망락모형적의합결과현시,뇌출혈사망솔적RNL위0 7967,MAPE 위0.19,MSE위0.21,MAE위0.18,P위82.53％,평균오차솔위0.17.결론 응용BP신경망락예측모형대2010년합이빈시뇌출혈사망솔진행예보,통과여다원선성회귀모형예보결과진행비교,표명해모형구유경고적예보준학도.
Objective Using the Back Propagation (BP) Neural Network Model to discover the relationship between meteorological factors and mortality of intracerebral hemorrhage,to provide evidence for developing an intracerebral hemorrhage prevention and control program,in Harbin.Methods Based on the characteristics of BP neural network,a neural network Toolbox of MATLAB 7.0 software was used to build Meteorological data of 2007-2009 with intracerebral hemorrhage mortality to predict the effect of BP neural network model,and to compare with the traditional multivariate linear regression model. Results Datas from the multivariate linear regrcssion indicated that the cerebral hemorrhage death mortality had a negative correlation with maximum temperatureand minimum humidity while having a positive correlation with the average relative humidity and the hours of sunshine.The linear correlation coefficient of intracerebral hemorrhage mortality was 0.7854,with mean absolute percentage (MAPE) as 0.21,mean square error (MSE) as 0.22,mean absolute error(MAE) as 0.19.The accuracy of forecasting was 81.31％ with an average error rate as 0.19.The Fitting results of BP neural network model showed that non-linear correlation coefficient of intracerebral hemorrhage mortality was 0.7967,with MAPE as 0.19,MSE as 0.21,MAE as 0.18.The forecasting accuracy was 82.53％ with the average error rate as 0.17.Conclusion The BP neural network model showed a higher forecasting accuracy when compared to the multiple linear regression model on intraccrebral hemorrhage mortality,using the data of 2010' s.