{"title":"可行性确定的动态抽样","authors":"Yijie Peng, Jie Song, Jie Xu, E. Chong","doi":"10.1109/COASE.2018.8560526","DOIUrl":null,"url":null,"abstract":"We formulate the sampling allocation decision for feasibility determination as a dynamic policy in a Bayesian setting. This new formulation addresses the limitations of previous static optimization formulation. In an approximate dynamic programming paradigm, we propose an approximately optimal allocation policy that maximizes a single-feature of the value function one-step ahead. Numerical results demonstrate the efficiency of the proposed method.","PeriodicalId":6518,"journal":{"name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","volume":"25 1","pages":"887-892"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Dynamic Sampling for Feasibility Determination\",\"authors\":\"Yijie Peng, Jie Song, Jie Xu, E. Chong\",\"doi\":\"10.1109/COASE.2018.8560526\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We formulate the sampling allocation decision for feasibility determination as a dynamic policy in a Bayesian setting. This new formulation addresses the limitations of previous static optimization formulation. In an approximate dynamic programming paradigm, we propose an approximately optimal allocation policy that maximizes a single-feature of the value function one-step ahead. Numerical results demonstrate the efficiency of the proposed method.\",\"PeriodicalId\":6518,\"journal\":{\"name\":\"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)\",\"volume\":\"25 1\",\"pages\":\"887-892\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COASE.2018.8560526\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COASE.2018.8560526","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We formulate the sampling allocation decision for feasibility determination as a dynamic policy in a Bayesian setting. This new formulation addresses the limitations of previous static optimization formulation. In an approximate dynamic programming paradigm, we propose an approximately optimal allocation policy that maximizes a single-feature of the value function one-step ahead. Numerical results demonstrate the efficiency of the proposed method.
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