{"title":"基于贝叶斯自适应加权的混合燃料消耗预测研究","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":"{\"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}","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}
Research on combined POL consumption forecast based on bayes adaptive weighting
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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