{"title":"Research on combined POL consumption forecast based on bayes adaptive weighting","authors":"Li Bixin, Li Heng, Yongdong Su, H. Jin","doi":"10.1109/GSIS.2015.7301919","DOIUrl":null,"url":null,"abstract":"In the future informatization battlefield with high technology, POL (petroleum, oil and lubrication) consumption is featured with openness, non-linear, dynamic, uncertainty and self-similarity. Based on Bayes, known probability distribution and deduction of observed data, this paper aims to conduct adaptive weighting for adaptive filtration forecast model; Case-Based-Reasoning (CBR) forecast model, and grey-fractal dimension forecast model. So, to form a combined POL consumption forecast model based on Bayes adaptive weighting, optimize the POL consumption forecast model, and improve the forecast precision of POL consumption.","PeriodicalId":246110,"journal":{"name":"2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Grey Systems and Intelligent Services (GSIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GSIS.2015.7301919","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In the future informatization battlefield with high technology, POL (petroleum, oil and lubrication) consumption is featured with openness, non-linear, dynamic, uncertainty and self-similarity. Based on Bayes, known probability distribution and deduction of observed data, this paper aims to conduct adaptive weighting for adaptive filtration forecast model; Case-Based-Reasoning (CBR) forecast model, and grey-fractal dimension forecast model. So, to form a combined POL consumption forecast model based on Bayes adaptive weighting, optimize the POL consumption forecast model, and improve the forecast precision of POL consumption.
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