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| Study on Atmospheric PM2.5 Soft Sensor Based on GA-BP Neural Network |
| ZHENG Hai-ming,SHANG Xiao-xiao |
| Department of Mechanical Engineering, North China Electric Power University, Hebei, Baoding 071003, China |
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Abstract 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.
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2014, Vol. 35 
