输入层自构造神经网络用于红外光谱多元校正

为了解决多组分红光谱定量分析中的特征的取和校正建模问题,本文提出了一种输入层自构造神经网络。在应用这种网络之前的预处理过程首先对训练数据进行分析,获得关于问题的某些先验知识。在训练阶段,神经网络根据先验知识自动选择输入层神经元的个数,同时确定网络参数。这种网络模型将特征提取和参数学习过程融为一体,有利于提高建模效率。利用仿真红外光谱的定量分析实验表明,这种网络模型不仅能够对光谱数据实现高效率的波长选择,并具有抑制随机噪声和非线性干扰的能力。

Input Layer Self-construction Neural Network and Its Use in Multivariant Calibration of Infrared Spectra

In order to solve the problems of feature extraction and calibration modelling in the area of quantitatively infrared spectra analysis, an input layer self constructive neural network (ILSC NN) is proposed. Before the NN training process, the training data is firstly analyzed and some prior knowledge about the problem is obtained. During the training process, the number of the input neurons is determined adaptively based on the prior knowledge. Meantime, the network parameters are also determined. This algorithm of the NN model helps to increase the efficiency of calibration modelling. The test experiment of quantitative analysis using simulated spectral data showed that this modelling method could not only achieve efficient wavelength selection, but also remarkably reduce the random and non linear noises.