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) ; click here to follow the link
Biblioteka Uniwersytetu Zielonogórskiego
Oct 14, 2021
Sep 15, 2021
161
https://zbc.uz.zgora.pl/publication/65820
Edition name | Date |
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Data-driven models for fault detection using kernel PCA: A water distribution system case study | Oct 14, 2021 |
Rienmüller, Theresa Hofbaur, Michael Travé-Massuy?s, Louise Bayoudh, Mehdi Korbicz, Józef - red. Uciński, Dariusz - red.
Chen, Wei Khan, Abdul Q. Abid, Muhammmad Ding, Steven X. Korbicz, Józef - red. Uciński, Dariusz - red.
Maksimov, Vyacheslav Pandolfi, Luciano Triggiani, Roberto- ed. Maksimov, Vyacheslav I. - ed.
Bilski, Piotr Wojciechowski, Jacek Makowski, Ryszard - ed. Zarzycki, Jan - ed.
Edwards, Christopher Alwi, Halim Tan, Chee Pin Korbicz, Józef - red. Uciński, Dariusz - red.
Janczak, Andrzej Korbicz, Józef - red. Patton, Ronald J. - red.
Troć, Maciej Unold, Olgierd Korbicz, Józef - red. Uciński, Dariusz - red.
Czarnowski, Ireneusz Jędrzejowicz, Piotr Korbicz, Józef - red. Uciński, Dariusz - red.