摘要
针对地表水质的复杂性以及紫外光谱数据维数高、谱带重叠严重的特点,提出将相关向量机算法应用于硝氮的连续紫外光谱分析,实现了对实际污水硝氮的快速准确无污染检测。首先介绍了相关向量机算法原理,然后在分析制药污水紫外吸收光谱的基础上选取230～245nm紫外吸光度数据用于建模,应用多元线性回归、偏最小二乘方法、经典支持向量机方法(SVM)和相关向量机方法分别建立硝氮回归模型并比较分析模型性能。实验结果表明:相关向量机模型预测更准确,模型更稀疏,预测速度快,检测结果的相对满量程误差控制在4.5%以内,适用于对复杂组成成分的实际污水硝氮的有效快速检测。
On account of the high dimension and band overlapping features of the ultraviolet spectrum of complex wastewater,the relevance vector machine（RVM） algorithm combined with contiguous ultraviolet spectrum technology was applied in nitrate modeling to realize the rapid and accurate prediction of nitrate-nitrogen.At first the algorithm principle of RVM was introduced,and then based on the ultraviolet spectra of collected pharmacy effluent samples,ultraviolet absorption data between 230 and 245 nm were selected for modeling.Multivariate linear regression,partial least squares,classical support vector machines（SVM） and RVM methods were applied in nitrate modeling respectively and model performances were compared.Experimental result indicates that RVM method has advantages of higher prediction accuracy,sparser model than other compared methods and faster operation speed than SVM method.The relative full-range error is less than 4.5%F.S..Finally,it can be concluded that the LS-SVM method is effective in rapid and accurate detection of nitrate in practical wastewater with complicated composition.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2013年第4期1048-1051,共4页
Spectroscopy and Spectral Analysis
基金
国家(863计划)项目(2009AA04Z327)
国际科技合作项目(2009DFB10440)资助
关键词
相关向量机
硝氮
水质监测
贝叶斯学习
紫外光谱
Relevance vector machine
Nitrate
Water monitoring
Bayesian learning
Ultraviolet spectrum
