CAJ | 학술논문

使用粗糙集理论对空气品质监测数据进行属性约简，构建BP神经网络模型中隐层的神经元节点、确定各节点之间的权值和网络结构，通过BP算法对污染物浓度数据进行迭代训练，求出BP神经网络的参数，实现空气品质预测。
사용조조집이론대공기품질감측수거진행속성약간，구건BP신경망락모형중은층적신경원절점、학정각절점지간적권치화망락결구，통과BP산법대오염물농도수거진행질대훈련，구출BP신경망락적삼수，실현공기품질예측。
This paper used the theory of rough set make attribute reduction to the air quality monitoring data. By using attribute reduction to determine the neuronsin node in the hidden layer of the neural network model and the weights of every nodes and make sure the network structure construction. By using BP algorithm itera-tive training on the pollutant concentration data,it can calculate the parameters of BP neural network and im-plementation of air quality forecast.