高温燃气红外光谱特征是判断燃气成分和浓度的有效途径.针对高温燃气红外辐射特性复杂、建模难度高的问题,研究了一种基于间隔偏最小二乘(interval Partial Least Squares,iPLS)和核 主成分分析(Kernel Principal Component Analysis,KPCA)的特征提取算法.首先通过iPLS进行预筛选,确定具有最优预测能力的特征光谱波段,避免单个子区间建模过程中有用吸收峰信息的遗失;其次,利用KPCA降低数据维度,保留贡献率高的关键特征,降低成分预测模型的复杂度.仿真结果表明,经过iPLS-KPCA方法特征提取后,预测模型的复杂度大幅下降,且预测能力显著提升.
Research on Infrared Spectral Feature Extraction Method for High Temperature Gas Based on iPLS-KPCA
The infrared spectrum characteristic of high temperature gas is an effective way to judge the com-position and concentration of gas.Aiming at the problems of complex infrared radiation characteristics and high modeling difficulty of high-temperature gas,a feature extraction algorithm based on interval partial least squares(iPLS)and kernel principal component analysis(KPCA)is studied.Firstly,the characteristic spectral bands with the best prediction ability are determined by pre-screening with iPLS to avoid the loss of useful ab-sorption peak information in the process of single subinterval modeling.Secondly,KPCA is used to reduce the data dimension,retain the key features with high contribution rate,and reduce the complexity of the compo-nent prediction model.The simulation results show that after feature extraction by iPLS-KPCA method,the complexity of the prediction model is greatly reduced,and the prediction ability is significantly improved.
high-temperature combustion gasinterval partial least squareskernel principal component anal-ysisfeature extraction
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维普
