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基于红外光谱信号处理的相关算法研究
其他题名Research on Some Algorithms About Infrared Spectrum
胡勇
学位类型工学博士
导师彭思龙
2013-05-30
学位授予单位中国科学院大学
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词红外光谱 偏最小二乘 模型转移 虚拟样本 集成学习 Infrared Spectrum Partial Least Squares Model Transfer Virtual Sample Ensemble Learning
摘要随着人们生活水平的不断提高,人们对食品质量的要求也越来越高,红外光谱分析技术作为一种快速、准确、灵敏、实用的产品品质检测方法已经被分析人员所接受。红外光谱分析是光谱测量技术、计算机技术与化学计量学技术的有机结合,利用化学计量学方法对采集的红外光谱建立校正模型,然后通过该模型对未知样本进行预测。在实际应用过程中,由于采集样本的不稳定性及外在条件的干扰,这些因素都会影响校正模型的建立。如何得到鲁棒的校正模型有效的应用于实际生产是一个急需解决的问题。在此背景下,本文研究了红外光谱信号处理的相关算法,探讨了利用红外光谱对复杂混合物做定性定量分析的相关问题,并提出了几个有意义的算法,这对于复杂混合物的质量控制有着重要的现实意义。 本文的创新成果主要有以下三点: (1)基于红外光谱数据的特点,本文研究了不平衡及高维小样本分类问题,提出了基于窗口回归的上采样方法生成虚拟样本。该算法充分考虑了样本的分布情况以及相邻样本之间在局部窗口的加性及乘性信息。实验结果表明:在不平衡样本分类问题中,与传统方法相比,该算法在一定程度上降低了少类样本错分的可能性;同时,在高维小样本分类问题中,相比传统的特征提取算法,该算法提高了分类正确率。 (2)我们研究了偏最小二乘算法的鲁棒性,提出了基于差异信息的集成偏最小二乘算法。该算法基于“偏差–方差–协方差”分解策略,通过引入虚拟样本来构造一系列差异子模型,同时引入折中参数保证子模型的正确性。实验结果表明:同传统方法相比,该算法克服了校正样本不足及受到噪声污染的问题,有效的提高了校正模型的推广能力。 (3)针对传统模型转移方法主要用于红外光谱定量分析的问题,我们首次提出了基于最大间隔准则的模型转移方法用于定性分析。该算法在求解转移矩阵的同时充分利用样本的标记信息,通过最大间隔准则尽量保持样本在主从仪器空间的结构一致性。同传统的模型转移算法及一些光谱预处理算法相比,该算法可以有效提高校正模型的定性分析能力。
其他摘要With the continuous improvement of people’s living standards, people are increasingly high requirements for food quality. Infrared analysis(IR)as a rapid detection method for accurate, sensitive, practical product quality has been accepted by the analysts. Infrared spectral technology is the combination of measurement technology, computer technology and chemometrics. We establish calibration model for collected infrared spectral with chemometrics method, then predict the unknown samples with the calibration model. While in the practical application of the process, due to the instability of collecting samples and the interference of external conditions, which will affect the calibration model. How to get robust calibration models used in actual production is an urgent problem. In this context, this paper research on some algorithms about infrared spectrum signal processing. We focus on the qualitative and quantitative analysis of complex mixtures by Infrared spectroscopy, and we propose several meaningful algorithms, it has important practical significance for quality control of complex mixtures. There are three main innovations of this paper: Based on the characteristics of the infrared spectral data, we focus on the unbalanced and high dimensional small sample classification problem, propose an improved algorithm based on window regression over-sampling(WRO)to generate virtual samples. The algorithm fully consider the distribution of samples as well as the additive and multiplicative information between adjacent samples in a local window. The experimental results show that: for the imbalanced data classification problem, compared with the traditional methods, the proposed algorithm can reduce the possibility of the minority class samples misclassified to some extent; For the high-dimensional small sample classification problems, compared with the traditional feature extraction methods, the proposed algorithm improves the classification accuracy. We research on the robustness of the partial least squares algorithm, pro-pose an improved ensemble algorithm creating diversity partial least squares (CDPLS). This algorithm is based on the “Bias - Variance - Covariance” decomposition, through the introduction of virtual samples to construct a series of diverse sub-models, and add a tradeoff parameter to ensure the correctness of the sub-models. Experimental results show that: Compared with the traditional methods, the proposed ...
馆藏号XWLW1840
其他标识符200918014628027
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/6544
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
胡勇. 基于红外光谱信号处理的相关算法研究[D]. 中国科学院自动化研究所. 中国科学院大学,2013.
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