布谷鸟搜索的润滑脂特征红外光谱波段优选技术
发布时间：2019-05-06 21:37
【摘要】：针对润滑脂分类,提出了基于布谷鸟搜索的红外光谱波段筛选方法,有效剔除了易受噪声等环境影响的红外光谱区域、实现了对庞大光谱数据进行特征选择和降维处理、通过筛选光谱最优波段建立了更加准确高效的润滑脂分类模型。以三类不同稠化剂润滑脂的红外光谱数据为研究对象,采用主成分分析法(PCA),对不同波段的红外光谱数据进行压缩,以提取的红外光谱主要成分作为输入,润滑脂稠化剂类别作为输出,通过布谷鸟搜索法(CS),对主要成分权重和分类核参数进行准确度寻优训练,建立分类识别预测模型。对所建立的模型再进行分类准确性测试,得到模型测试结果准确度,建立红外光谱波段和测试准确度之间的联系,得到润滑脂最优类别识别模型和最优分类波段。对所建立的模型再进行分类准确性测试,结果显示:经过布谷鸟搜索法训练加权后的主要特征呈现明显聚类现象,可以得到分类核,实现对润滑脂种类的准确识别;在搜索过程中提供了区分不同润滑脂的推荐波段和特征峰,使对润滑脂的正确鉴别概率由全波段建立分类模型的94.44%提高到筛选后特征波段建立分类模型的100%,并减少了运算时间、提高了搜索运行效率。
[Abstract]:Aiming at grease classification, an infrared spectral band screening method based on cuckoo search was proposed, which effectively eliminated the infrared spectral region which was susceptible to noise and other environmental effects, and realized the feature selection and dimensionality reduction of huge spectral data. A more accurate and efficient grease classification model was established by screening the spectral optimum band. Taking infrared spectrum data of three kinds of thickener greases as the research object, the infrared spectrum data of different bands were compressed by principal component analysis (PCA), and the main components of the extracted infrared spectra were used as input. The classification of grease thickener is used as output, and the weight of main components and the kernel parameters of classification are trained by cuckoo search method (CS), and the model of classification identification and prediction is established. The accuracy of the model is tested and the relationship between the infrared spectrum band and the test accuracy is established. The optimal classification model and the optimal classification band of lubricating grease are obtained. The classification accuracy of the model is tested. The results show that the main features after training and weighting by cuckoo search show obvious clustering phenomenon, and the classification kernel can be obtained to realize the accurate identification of grease types. In the process of searching, the recommended bands and characteristic peaks are provided to distinguish different greases, so that the correct identification probability of grease is increased from 94.44% of full-band classification model to 100% of the selected characteristic band classification model. And reduce the operation time, improve the efficiency of search.
【作者单位】： 华北电力大学能源动力与机械工程学院;
【基金】：国家自然科学基金项目(5157181) 北京市自然科学基金项目(2172053)资助
【分类号】：O657.33;TE626.4
，
本文编号：2470513
[Abstract]:Aiming at grease classification, an infrared spectral band screening method based on cuckoo search was proposed, which effectively eliminated the infrared spectral region which was susceptible to noise and other environmental effects, and realized the feature selection and dimensionality reduction of huge spectral data. A more accurate and efficient grease classification model was established by screening the spectral optimum band. Taking infrared spectrum data of three kinds of thickener greases as the research object, the infrared spectrum data of different bands were compressed by principal component analysis (PCA), and the main components of the extracted infrared spectra were used as input. The classification of grease thickener is used as output, and the weight of main components and the kernel parameters of classification are trained by cuckoo search method (CS), and the model of classification identification and prediction is established. The accuracy of the model is tested and the relationship between the infrared spectrum band and the test accuracy is established. The optimal classification model and the optimal classification band of lubricating grease are obtained. The classification accuracy of the model is tested. The results show that the main features after training and weighting by cuckoo search show obvious clustering phenomenon, and the classification kernel can be obtained to realize the accurate identification of grease types. In the process of searching, the recommended bands and characteristic peaks are provided to distinguish different greases, so that the correct identification probability of grease is increased from 94.44% of full-band classification model to 100% of the selected characteristic band classification model. And reduce the operation time, improve the efficiency of search.
【作者单位】： 华北电力大学能源动力与机械工程学院;
【基金】：国家自然科学基金项目(5157181) 北京市自然科学基金项目(2172053)资助
【分类号】：O657.33;TE626.4
，
本文编号：2470513
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