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  • 标题:A Fuzzy Data-Driven Paradigmatic Predictor
  • 本地全文:下载
  • 作者:Farzad Amirjavid ; Hamidreza Nemati ; Sasan Barak
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2019
  • 卷号:52
  • 期号:13
  • 页码:2366-2371
  • DOI:10.1016/j.ifacol.2019.11.560
  • 语种:English
  • 出版社:Elsevier
  • 摘要:Data-driven prediction of future events is to provide decision-makers Predictive Information (PI) to decrease human-error. They usually desire possession of a predictor which works independently from the humanized configurations and works efficiently and accurately. The accurate data-driven prediction of the systems’ behavior is the primary focus of this paper. We define the future state of a system is a set of uncertain values, which can be modeled by fuzzy numbers. The future machine state is not very dissimilar to the current status, and the next event is a sort of behavior repetition. The PI also justifies the system being in a trend to achieve a goal, and it counts the unplanned contextual reactions of the system. In this paper, we come up with a fuzzy data-driven predictor application to foretell the system behavior.
  • 关键词:KeywordsFuzzy logictemporal data analyticsadaptive learningsystems theory
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