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首页> 外文期刊>Journal of Computers >An Improved Back Propagation Neural Network Model and Its Application
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An Improved Back Propagation Neural Network Model and Its Application

机译：改进的后传播神经网络模型及其应用

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—t-Stroke is one of the most serious disease, and the incidence rate of stroke is confirmed to be related to environmental factors including temperature, pressure and humidity .In order to obtain the relationship between the incidence rate and environmental factors , we research on local daily meteorological data and stroke disease cases from January 2008 to December 2012, which is provided by the administrative department of public health and medical institutions statistics in China, then build the improved BPNN(Back propagation neural network) model to carry out data analysis and processing, obtain the weight matrix between them. It can be seen that the relationship between incidence rate and pressure is the highest degree from the value of weight matrix, and pressure is positive correlation with the incidence rate. The relationship between the temperature and incidence rate is second, and they are negative correlation. The incidence between average relative humidity and correlation is quite small. The results show that the model can be used to predict the future stroke incidence rate under various meteorological conditions, and it can play a certain role in making disease knowledge popular and providing a reference to potential patients.
机译：-T-中风是最严重的疾病之一，并且证实中风的发病率与环境因素有关，包括温度，压力和湿度。为了获得发病率和环境因素之间的关系，我们研究2008年1月至2012年1月至2012年12月的当地日常气象病例，由中国公共卫生和医疗机构统计数据提供，然后建立改进的BPNN（后传播神经网络）模型来进行数据分析和处理，获得它们之间的权重矩阵。可以看出，发射率和压力之间的关系是重量矩阵值的最高程度，压力与发病率正相关。温度和发射率之间的关系是秒，它们是负相关的。平均相对湿度和相关性之间的发生率非常小。结果表明，该模型可用于预测在各种气象条件下的未来行程发病率，并且可以在使疾病知识流行和提供对潜在患者的提及方面发挥一定的作用。

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