Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Set membership estimation of parameters and variables in dynamic networks by recursive algorithms with a moving measurement window

100%

Duzinkiewicz K.

International Journal of Applied Mathematics and Computer Science

2006

tom 16

nr 2

209-217

The paper considers a set membership joint estimation of variables and parameters in complex dynamic networks based on parametric uncertain models and limited hard measurements. A recursive estimation algorithm with a moving measurement window is derived that is suitable for on-line network monitoring. The window allows stabilising the classic recursive estimation algorithm and significantly improves estimate tightness. The estimator is validated on a case study regarding a water distribution network. Tight set estimates of unmeasured pipe flows, nodal heads, tank level and pipe resistances are obtained.

Data-driven models for fault detection using kernel PCA: A water distribution system case study

51%

Nowicki A., Grochowski M., Duzinkiewicz K.

International Journal of Applied Mathematics and Computer Science

2012

tom 22

nr 4

939-949

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.

Ograniczanie wyników

2 International Journal of Applied Mathematics and Computer Science

2 Duzinkiewicz K.

1 Grochowski M.

1 Nowicki A.

1 2012

1 2006

Wyniki wyszukiwania

Set membership estimation of parameters and variables in dynamic networks by recursive algorithms with a moving measurement window

Data-driven models for fault detection using kernel PCA: A water distribution system case study