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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - A Fuzzy Data-Driven Paradigmatic Predictor
AU - Amirjavid, Farzad
AU - Nemati, Hamidreza
AU - Barak, Sasan
PY - 2019/12/31
Y1 - 2019/12/31
N2 - Data-driven prediction of future events is to provide decision-makers PredictiveInformation (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.
AB - Data-driven prediction of future events is to provide decision-makers PredictiveInformation (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.
KW - fuzzy logic
KW - Temporal data analytics
KW - Adaptive learning
KW - Systems theory
U2 - 10.1016/j.ifacol.2019.11.560
DO - 10.1016/j.ifacol.2019.11.560
M3 - Journal article
VL - 52
SP - 2366
EP - 2371
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
SN - 2405-8963
IS - 13
ER -