{"title":"带反向传播的ptSTL公式的学习参数","authors":"Ahmet Ketenci, Ebru Aydin Gol","doi":"10.1109/SIU49456.2020.9302093","DOIUrl":null,"url":null,"abstract":"In this paper, a backpropagation based algorithm is presented to learn parameters of past time Signal Temporal Logic (ptSTL) formulas. A differentiable weight matrix over the parameter values and a loss function based on the mismatch value of the corresponding formulas over the labeled dataset are used in the algorithm. Analysis over a sample dataset shows that the algorithm solves the ptSTL parameter synthesis problem in an efficient way.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Learning Parameters of ptSTL Formulas with Backpropagation\",\"authors\":\"Ahmet Ketenci, Ebru Aydin Gol\",\"doi\":\"10.1109/SIU49456.2020.9302093\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a backpropagation based algorithm is presented to learn parameters of past time Signal Temporal Logic (ptSTL) formulas. A differentiable weight matrix over the parameter values and a loss function based on the mismatch value of the corresponding formulas over the labeled dataset are used in the algorithm. Analysis over a sample dataset shows that the algorithm solves the ptSTL parameter synthesis problem in an efficient way.\",\"PeriodicalId\":312627,\"journal\":{\"name\":\"2020 28th Signal Processing and Communications Applications Conference (SIU)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 28th Signal Processing and Communications Applications Conference (SIU)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIU49456.2020.9302093\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 28th Signal Processing and Communications Applications Conference (SIU)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIU49456.2020.9302093","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Learning Parameters of ptSTL Formulas with Backpropagation
In this paper, a backpropagation based algorithm is presented to learn parameters of past time Signal Temporal Logic (ptSTL) formulas. A differentiable weight matrix over the parameter values and a loss function based on the mismatch value of the corresponding formulas over the labeled dataset are used in the algorithm. Analysis over a sample dataset shows that the algorithm solves the ptSTL parameter synthesis problem in an efficient way.
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