{"title":"用于缺失数据估算、不确定性量化和纳入系统识别的两阶段非参数框架","authors":"Wen-Jing Zhang, Ka-Veng Yuen, Wang-Ji Yan","doi":"10.1111/mice.13237","DOIUrl":null,"url":null,"abstract":"In many engineering applications, missing data during system identification can hinder the performance of the identified model. In this paper, a novel two-stage nonparametric framework is proposed for missing data imputation, uncertainty quantification, and its integration in system identification with reduced computational complexity. The framework does not require functional forms for both the imputation model and the identified mathematical model. Moreover, through the construction of a single imputation model, analytical expressions of predictive distributions can be given for missing entries across all missingness patterns. Furthermore, analytical expressions of the expectation and variance of distribution are provided to impute missing values and quantify uncertainty, respectively. This uncertainty is incorporated into a single mathematical model by mitigating the influence of samples with imputations during training and testing. The framework is applied to three applications, including a simulated example and two real applications on structural health monitoring and seismic attenuation modeling. Results reveal a minimum reduction of 21% in root mean squared error values, compared to those achieved by directly removing incomplete samples.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":8.5000,"publicationDate":"2024-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Two-stage nonparametric framework for missing data imputation, uncertainty quantification, and incorporation in system identification\",\"authors\":\"Wen-Jing Zhang, Ka-Veng Yuen, Wang-Ji Yan\",\"doi\":\"10.1111/mice.13237\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In many engineering applications, missing data during system identification can hinder the performance of the identified model. In this paper, a novel two-stage nonparametric framework is proposed for missing data imputation, uncertainty quantification, and its integration in system identification with reduced computational complexity. The framework does not require functional forms for both the imputation model and the identified mathematical model. Moreover, through the construction of a single imputation model, analytical expressions of predictive distributions can be given for missing entries across all missingness patterns. Furthermore, analytical expressions of the expectation and variance of distribution are provided to impute missing values and quantify uncertainty, respectively. This uncertainty is incorporated into a single mathematical model by mitigating the influence of samples with imputations during training and testing. The framework is applied to three applications, including a simulated example and two real applications on structural health monitoring and seismic attenuation modeling. Results reveal a minimum reduction of 21% in root mean squared error values, compared to those achieved by directly removing incomplete samples.\",\"PeriodicalId\":156,\"journal\":{\"name\":\"Computer-Aided Civil and Infrastructure Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.5000,\"publicationDate\":\"2024-05-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer-Aided Civil and Infrastructure Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1111/mice.13237\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer-Aided Civil and Infrastructure Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1111/mice.13237","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Two-stage nonparametric framework for missing data imputation, uncertainty quantification, and incorporation in system identification
In many engineering applications, missing data during system identification can hinder the performance of the identified model. In this paper, a novel two-stage nonparametric framework is proposed for missing data imputation, uncertainty quantification, and its integration in system identification with reduced computational complexity. The framework does not require functional forms for both the imputation model and the identified mathematical model. Moreover, through the construction of a single imputation model, analytical expressions of predictive distributions can be given for missing entries across all missingness patterns. Furthermore, analytical expressions of the expectation and variance of distribution are provided to impute missing values and quantify uncertainty, respectively. This uncertainty is incorporated into a single mathematical model by mitigating the influence of samples with imputations during training and testing. The framework is applied to three applications, including a simulated example and two real applications on structural health monitoring and seismic attenuation modeling. Results reveal a minimum reduction of 21% in root mean squared error values, compared to those achieved by directly removing incomplete samples.
期刊介绍:
Computer-Aided Civil and Infrastructure Engineering stands as a scholarly, peer-reviewed archival journal, serving as a vital link between advancements in computer technology and civil and infrastructure engineering. The journal serves as a distinctive platform for the publication of original articles, spotlighting novel computational techniques and inventive applications of computers. Specifically, it concentrates on recent progress in computer and information technologies, fostering the development and application of emerging computing paradigms.
Encompassing a broad scope, the journal addresses bridge, construction, environmental, highway, geotechnical, structural, transportation, and water resources engineering. It extends its reach to the management of infrastructure systems, covering domains such as highways, bridges, pavements, airports, and utilities. The journal delves into areas like artificial intelligence, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, internet-based technologies, knowledge discovery and engineering, machine learning, mobile computing, multimedia technologies, networking, neural network computing, optimization and search, parallel processing, robotics, smart structures, software engineering, virtual reality, and visualization techniques.