{"title":"基于袋式随机配置网络的频谱数据分析","authors":"","doi":"10.1016/j.infrared.2024.105609","DOIUrl":null,"url":null,"abstract":"<div><div>Although stochastic configuration networks(SCN) has the universal approximation property and faster learning speed,during the process of model construction,the randomness of weight and biases assignment as well as the uncertainty of model structure lead to instability. Inspired by bagging,an ensemble model named bagging SCN is proposed to address the limitation of the single model. Firstly,multiple different training subsets are extracted by bootstrap sampling. Then the SCN submodels are trained on each subset. Finally,the median output of these submodels is taken as the final prediction. Predictions made by bagging SCN are tested on two public datasets. The performance of bagging SCN is then compared with other techniques,including SCN,bagging SCN with other aggregating rules. Experimental results demonstrate that bagging SCN proposed in this study exhibits good stability and high prediction accuracy,making it suitable for quantitative analysis of spectral data.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spectral data analysis based on bagging stochastic configuration networks\",\"authors\":\"\",\"doi\":\"10.1016/j.infrared.2024.105609\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Although stochastic configuration networks(SCN) has the universal approximation property and faster learning speed,during the process of model construction,the randomness of weight and biases assignment as well as the uncertainty of model structure lead to instability. Inspired by bagging,an ensemble model named bagging SCN is proposed to address the limitation of the single model. Firstly,multiple different training subsets are extracted by bootstrap sampling. Then the SCN submodels are trained on each subset. Finally,the median output of these submodels is taken as the final prediction. Predictions made by bagging SCN are tested on two public datasets. The performance of bagging SCN is then compared with other techniques,including SCN,bagging SCN with other aggregating rules. Experimental results demonstrate that bagging SCN proposed in this study exhibits good stability and high prediction accuracy,making it suitable for quantitative analysis of spectral data.</div></div>\",\"PeriodicalId\":13549,\"journal\":{\"name\":\"Infrared Physics & Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-10-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Infrared Physics & Technology\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1350449524004936\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"INSTRUMENTS & INSTRUMENTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Infrared Physics & Technology","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1350449524004936","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
Spectral data analysis based on bagging stochastic configuration networks
Although stochastic configuration networks(SCN) has the universal approximation property and faster learning speed,during the process of model construction,the randomness of weight and biases assignment as well as the uncertainty of model structure lead to instability. Inspired by bagging,an ensemble model named bagging SCN is proposed to address the limitation of the single model. Firstly,multiple different training subsets are extracted by bootstrap sampling. Then the SCN submodels are trained on each subset. Finally,the median output of these submodels is taken as the final prediction. Predictions made by bagging SCN are tested on two public datasets. The performance of bagging SCN is then compared with other techniques,including SCN,bagging SCN with other aggregating rules. Experimental results demonstrate that bagging SCN proposed in this study exhibits good stability and high prediction accuracy,making it suitable for quantitative analysis of spectral data.
期刊介绍:
The Journal covers the entire field of infrared physics and technology: theory, experiment, application, devices and instrumentation. Infrared'' is defined as covering the near, mid and far infrared (terahertz) regions from 0.75um (750nm) to 1mm (300GHz.) Submissions in the 300GHz to 100GHz region may be accepted at the editors discretion if their content is relevant to shorter wavelengths. Submissions must be primarily concerned with and directly relevant to this spectral region.
Its core topics can be summarized as the generation, propagation and detection, of infrared radiation; the associated optics, materials and devices; and its use in all fields of science, industry, engineering and medicine.
Infrared techniques occur in many different fields, notably spectroscopy and interferometry; material characterization and processing; atmospheric physics, astronomy and space research. Scientific aspects include lasers, quantum optics, quantum electronics, image processing and semiconductor physics. Some important applications are medical diagnostics and treatment, industrial inspection and environmental monitoring.