波长优选BP神经网络用于近红外光谱分析

摘要

Near Infrared Spectroscopy has been widely used in material composition analysis. In this self-designed filter NIR device,the article presents a modeling method based on BP neural network with wavelength optimization selection. First, multiple linear regression algorithm modeling is used to analyze the prepared 26 samples to obtain optimal wavelength which is then used as BP neural network model's input. After the calculation, it shows that the fitting residual is 8. 768991 x 10 "6 and the correlation coefficients of modeling samples and testing samples are respectively 0.994 and 0. 996. The experimental result indicates that the BP neural network modeling method based on wavelength optimization can gain the optimal solution more quickly,reduce the variables used in modeling and apparently improve the robustness of the quantitative analysis model, enhance the actual ability of detecting.%近红外光谱分析技术在物质成分分析中的得到广泛的应用,在自主研发的滤光片型近红外仪器中应用基于波长优选的BP神经网络模型的方法.该方法是采用多元线性回归算法获取最优波长,将最优波长作为BP神经网络模型的输入,所得模型的拟合残差为8.768991×10-6,建模样品集相关系数和检验样品集相关系数分别为0.994和0.996.试验结果表明,基于波长优选的BP神经网络模型方法能够更快获得最优解,减少建模所用变量,建立稳健的定量模型.