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A Fuzzy Data-Driven Paradigmatic Predictor

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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A Fuzzy Data-Driven Paradigmatic Predictor. / Amirjavid, Farzad; Nemati, Hamidreza; Barak, Sasan.
In: IFAC-PapersOnLine, Vol. 52, No. 13, 31.12.2019, p. 2366-2371.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Amirjavid, F, Nemati, H & Barak, S 2019, 'A Fuzzy Data-Driven Paradigmatic Predictor', IFAC-PapersOnLine, vol. 52, no. 13, pp. 2366-2371. https://doi.org/10.1016/j.ifacol.2019.11.560

APA

Amirjavid, F., Nemati, H., & Barak, S. (2019). A Fuzzy Data-Driven Paradigmatic Predictor. IFAC-PapersOnLine, 52(13), 2366-2371. https://doi.org/10.1016/j.ifacol.2019.11.560

Vancouver

Amirjavid F, Nemati H, Barak S. A Fuzzy Data-Driven Paradigmatic Predictor. IFAC-PapersOnLine. 2019 Dec 31;52(13):2366-2371. doi: 10.1016/j.ifacol.2019.11.560

Author

Amirjavid, Farzad ; Nemati, Hamidreza ; Barak, Sasan. / A Fuzzy Data-Driven Paradigmatic Predictor. In: IFAC-PapersOnLine. 2019 ; Vol. 52, No. 13. pp. 2366-2371.

Bibtex

@article{af949d5e9e8c46f19b1660c0e5246a58,
title = "A Fuzzy Data-Driven Paradigmatic Predictor",
abstract = "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.",
keywords = "fuzzy logic, Temporal data analytics, Adaptive learning, Systems theory",
author = "Farzad Amirjavid and Hamidreza Nemati and Sasan Barak",
year = "2019",
month = dec,
day = "31",
doi = "10.1016/j.ifacol.2019.11.560",
language = "English",
volume = "52",
pages = "2366--2371",
journal = "IFAC-PapersOnLine",
issn = "2405-8963",
publisher = "IFAC Secretariat",
number = "13",

}

RIS

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 -