基于偏最小二乘增量式神经网络的近红外光谱定量分析模型

提出了一种基于偏最小二乘增量式神经网络的近红外光谱定量分析模型。该模型采用典型三层反向传播神经网络(BPNN)，不同波长吸光度和成分浓度是模型的输入和输出。在使用历史样本训练之前先进行偏最小二乘(PLS)回归，所得自变量和因变量的历史负荷矩阵分别用于确定模型输入层和输出层的初始权值，且自变量的主成分个数作为隐层的节点数。当获得新的样本时，对新数据与历史负荷矩阵组合后进行PLS回归，将所得新的负荷矩阵与历史负荷矩阵融合后作为模型输入层和输出层新的初始权值，接着使用新样本对模型进行训练来实现增量式更新。将所提模型与PLS、BPNN、基于PLS的BPNN、递归PLS在天然气燃烧烟气近红外光谱数据上测定后比较。对于烟气中二氧化碳浓度的预测，所提模型的预测均方根误差(RMSEP)分别降低了27.27%，58.12%，19.24%和14.26%;对于烟气中一氧化碳浓度的预测，所提模型的RMSEP分别降低了20.65%，24.69%，18.54%和19.42%;对于烟气中甲烷浓度的预测，此模型的RMSEP分别降低了27.56%，37.76%，8.63%和3.20%。实验结果表明，所提模型不仅通过PLS对BPNN结构和初始权重的优化，使模型具有较强的预测能力，而且能在已建模型信息的基础上，不访问旧数据而用新增样本即可完成自身的增量式更新，从而使模型具有较好的稳健性和泛化性。

Near Infrared Spectroscopy Quantitative Analysis Model Based on Incremental Neural Network with Partial Least Squares

This paper proposes an near infrared spectroscopy quantitative analysis model based on incremental neural network with partial least squares. The proposed model adopts the typical three-layer back-propagation neural network (BPNN), and the absorbance of different wavelengths and the component concentration are the inputs and the outputs, respectively. Partial least square (PLS) regression is performed on the history training samples firstly, and the obtained history loading matrices of the independent variables and the dependent variables are used for determining the initial weights of the input layer and the output layer, respectively. The number of the hidden layer nodes is set as the number of the principal components of the independent variables. After a set of new training samples is collected, PLS regression is performed on the combination dataset consisting of the new samples and the history loading matrices to calculate the new loading matrices. The history loading matrices and the new loading matrices are fused to obtain the new initial weights of the input layer and the output layer of the proposed model. Then the new samples are used for training the proposed mode to realize the incremental update. The proposed model is compared with PLS, BPNN, the BPNN based on PLS (PLS-BPNN) and the recursive PLS (RPLS) by using the spectra data of flue gas of natural gas combustion. For the concentration prediction of the carbon dioxide in the flue gas, the root mean square error of prediction (RMSEP) of the proposed model are reduced by 27.27%, 58.12%, 19.24% and 14.26% than those of PLS, BPNN, PLS-BPNN and RPLS, respectively. For the concentration prediction of the carbon monoxide in the flue gas, the RMSEP of the proposed model are reduced by 20.65%, 24.69%, 18.54% and 19.42% than those of PLS, BPNN, PLS-BPNN and RPLS, respectively. For the concentration prediction of the methane in the flue gas, the RMSEP of the proposed model are reduced by 27.56%, 37.76%, 8.63% and 3.20% than those of PLS, BPNN, PLS-BPNN and RPLS, respectively. Experiments results show that the proposed model could optimize the construction and the initial weights of BPNN by PLS and has higher prediction effectiveness. Moreover, based on the information of the built model, the proposed model uses the new samples for incremental update without accessing the history samples. Hence, the proposed model has better robustness and generalization.