Cordón, Oskar - ed. ; Kazienko, Przemysław - ed.
Hybrid and Ensemble Methods in Machine Learning
Kernel Principal Component Analysis (KPCA), an example of machine learning, can be considered a non-linear extension of the PCA method. While various applications of KPCA are known, this paper explores the possibility to use it for building a data-driven model of a non-linear system-the water distribution system of the Chojnice town (Poland). ; This model is utilised for fault detection with the emphasis on water leakage detection. A systematic description of the system?s framework is followed by evaluation of its performance. Simulations prove that the presented approach is both flexible and efficient.
Zielona Góra: Uniwersytet Zielonogórski
AMCS, Volume 22, Number 4 (2012) ; kliknij tutaj, żeby przejść
Biblioteka Uniwersytetu Zielonogórskiego
2021-10-14
2021-09-15
191
https://zbc.uz.zgora.pl/repozytorium/publication/65820
Nazwa wydania | Data |
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Data-driven models for fault detection using kernel PCA: A water distribution system case study | 2021-10-14 |
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Troć, Maciej Unold, Olgierd Korbicz, Józef (1951- ) - red. Uciński, Dariusz - red.
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