传感技术学报2017，Vol.30Issue(3)：360-367,8.DOI:10.3969/j.issn.1004-1699.2017.03.005
基于PSO-BP神经网络的气溶胶质量浓度测量系统湿度补偿
The Humidity Compensation for Measurement Systems of Aerosol Mass Concentrations Based on the PSO-BP Neural Network
摘要
Abstract
Aerosol mass concentration is a key indicator of air quality.The influence of the changes in ambient humidity on aerosol mass concentration measurement based on single particle light scattering is large.Especially,physical characteristics and refractive index of particulate matter could change accordingly in high humidity,which uses system calibration parameters under low humidity conditions to invert aerosol mass concentration and leads to considerable error.Therefore,considering the above reasons,a compensation model is presented by particle swarm optimization BP neural network for data fusion of the measurement data.The experimental measurements show that after humidity compensation based on the PSO-BP neural network,the measurement error caused by high relative humidity decreases from the original approximately-10%~45% to-5%~30%,and the overall average relative error is reduced by 10%,which indicates that the PSO-BP method can reduce relative humidity effect on the aerosol mass concentration measurement system and improves system measurement accuracy effectively.关键词
气溶胶质量浓度/光散射/湿度补偿/PSO-BP神经网络Key words
aerosol mass concentration/light scattering/humidity compensation/PSO-BP neural network分类
信息技术与安全科学引用本文复制引用
张加宏,刘毅,顾芳,沈雷,冒晓莉,吴佳伟,汪程,包志伟..基于PSO-BP神经网络的气溶胶质量浓度测量系统湿度补偿[J].传感技术学报,2017,30(3):360-367,8.基金项目
国家自然科学基金项目(61307113,61306138,41605120) （61307113,61306138,41605120）
江苏省自然科学基金项目(BK2012460) （BK2012460）
江苏省高等学校大学生实践创新训练计划项目(201510300034) （201510300034）
江苏高校品牌专业建设工程资助项目(TAPP) （TAPP）
