基于GA-BP神经网络大气中PM2.5软测量研究

Study on Atmospheric PM2. 5 Soft Sensor Based on GA-BP NeuraI Network

大气中PM2.5质量浓度变化具有较强的非线性特性，传统的软测量方法很难对其做出准确的计量监测。针对传统BP神经网络易陷入局部最小值的缺陷，将遗传算法和BP神经网络相结合建立了GA-BP神经网络软测量模型，将该模型应用到大气PM2.5质量浓度的计量监测中，并与传统BP神经网络模型的监测结果进行对比，结果表明经过遗传算法优化后的模型具有更好的非线性拟合能力和更高的监测精度。

Because of the varying concentration of atmospheric PM2. 5 have strong nonlinear characteristics, traditional soft sensor methods are difficult to make accurate measuring and monitoring. According to traditional BP neural network is easy to fall into local minimum, BP neural network is combined with genetic algorithm to establish the GA-BP neural network soft sensor model. The model is applied to the monitoring of the atmospheric concentration of PM2. 5, and compared with the results of the monitoring of the traditional BP neural network model, the results show that the genetic algorithm optimization model has a better non-linear fitting ability and higher monitoring accuracy.