计算机应用研究2017，Vol.34Issue(10)：3020-3023,4.DOI:10.3969/j.issn.1001-3695.2017.10.032
随机子空间深度回归方法在紫外光谱水质分析中的应用
Random subspace deep regression and its application in water quality analysis of TOC in UV spectroscopy
摘要
Abstract
There are the problems of large quantities and high dimensionality in the analysis of TOC concentration by ultraviolet spectrometry.To solve these problems,this paper proposed a TOC analysis method based on random subspace deep regression.Firstly,the proposed method preprocessed the ultraviolet spectral data of TOC standard solutions to obtain the absorbance data.Then,in the high-dimensional space,it randomly selected the low-dimensional subspace to construct different feature subsets,and extracted the features of each subset by using the deep belief network.Finally,it established the TOC concentration inversion model by BP neural network,which was trained with the discriminant features.Experimental results based on the water quality analysis platform show that the relative errors of TOC concentration inversion results by the proposed method are less than 1％,and its stability is superior to the traditional water quality analysis methods.关键词
紫外光谱法/随机子空间/深度信念网络/BP神经网络Key words
ultraviolet spectrometry/random subspace/deep belief network/BP neural network分类
信息技术与安全科学引用本文复制引用
黄鸿,石光耀,金莹莹,何凯..随机子空间深度回归方法在紫外光谱水质分析中的应用[J].计算机应用研究,2017,34(10):3020-3023,4.基金项目
国家自然科学基金资助项目(41371338) （41371338）
重庆市研究生科研创新项目(CYS16040) （CYS16040）
