基于卷积神经网络的红外光谱建模分析综述

A Review of Infrared Spectrum Modeling Based on Convolutional Neural Networks

红外光谱技术存在着数据预处理复杂、预测精度不高,且难以处理大量非线性数据的问题,适于用卷积神经网络进行处理。本文首先分析了卷积神经网络应用在红外光谱上的优点,并对卷积神经网络结构组成进行简单的概述。然后针对卷积神经网络在光谱分析建模中的输入数据维度问题进行详细阐述;针对模型设计中卷积核参数的影响、多任务处理模型以及训练过程中的优化方法进行综述。最后分析了该研究的优点与不足,并展望了未来的发展趋势。

Convolutional neural networks are used to solve problems such as complex data preprocessing,low prediction accuracy,and difficulty in dealing with a large amount of nonlinear data in infrared spectroscopy.Moreover,owing to their strong feature extraction ability and good nonlinear expression ability,the application of convolutional neural networks in the modeling of infrared spectrum analysis has attracted attention.In this study,the advantages of the application of a convolutional neural network for the infrared spectrum are analyzed,and the structure and composition of the convolutional neural network are briefly summarized.Then,the dimension problem of the input data in the spectral analysis modeling of the convolutional neural network is described in detail.This paper reviews the influence of convolution kernel parameters in the model design,multi-task processing model,and optimization methods in the training process.Finally,the advantages and disadvantages of this research are analyzed,and future development trends are discussed.