| 基于iPLS-KPCA的高温燃气红外光谱特征提取方法研究 |
| 投稿时间：2024-03-28 修订日期：2024-04-15 点此下载全文 |
| 引用本文：席剑辉,许壮壮.基于iPLS-KPCA的高温燃气红外光谱特征提取方法研究[J].红外,2024,45(10):38~44 |
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| 基金项目:辽宁省自然科学基金项目(2015020069; 2015020061)；沈阳市科技创新团队项目(src201204) |
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| 中文摘要: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。 |
| 中文关键词:高温燃气 间隔偏最小二乘 核主成分分析 特征提取 |
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| Research on Infrared Spectral Feature Extraction Method for High Temperature Gas Based on iPLS-KPCA |
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| Abstract:The infrared spectrum characteristic of high temperature gas is an effective way to judge the composition 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 absorption 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 component 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. |
| keywords:high-temperature combustion gas interval partial least squares kernel principal component analysis feature extraction |
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