测试技术学报2018，Vol.32Issue(3)：191-196,6.DOI:10.3969/j.issn.1671-7449.2018.03.002
基于BP-RBF组合神经网络的废气监测盲区SO2浓度预测
Prediction of SO2 Concentration in the Blind Area of Exhaust Gas Monitoring Based on BP-RBF Combined Neural Network
摘要
Abstract
In the management of industrial park atmospheric environment,it is a significant issue to an-alyze the exhaust gas concentration in the blind monitoring area by the current monitoring system.A combinatorial neutral network was proposed to predict the exhaust gas concentration in the blind monito-ring area with the known monitoring information.Firstly,a neutral network structure was introduced to combine BP and RBF neural networks according to their characteristics.Secondly,the prediction prob-lem of monitoring the exhaust gas concentration was analyzed in the blind area,and the algorithm of prediction model was presented based on BP-RBF combinatorial neural network.Finally,the experiment was conducted to validate the proposed prediction method with the practical monitoring data of SO2con-centration in an industrial park.The experiment result indicates that the prediction method in this paper has good performance.And it is suitable for monitoring the prediction of waste gas concentration in the blind area.关键词
BP-RBF组合神经网络/废气监测/监测盲区/SO2浓度预测Key words
BP-RBF combined neural network/exhaust gas monitoring/monitor blind area/prediction of SO2gas concentration分类
信息技术与安全科学引用本文复制引用
李晓云,王晓凯..基于BP-RBF组合神经网络的废气监测盲区SO2浓度预测[J].测试技术学报,2018,32(3):191-196,6.基金项目
山西大学-小店区产学研合作项目 （）
