Temat: Outliers - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: An outlier-robust neuro-fuzzy system for classification and regression
Autorzy:: Siminski, Krzysztof
Powiązania:: https://bibliotekanauki.pl/articles/1838201.pdf
Data publikacji:: 2021
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: outliers
neuro-fuzzy system
clustering algorithm
regression
wyjątki
system neurorozmyty
algorytm grupowania
Opis:: Real life data often suffer from non-informative objects—outliers. These are objects that are not typical in a dataset and can significantly decline the efficacy of fuzzy models. In the paper we analyse neuro-fuzzy systems robust to outliers in classification and regression tasks. We use the fuzzy c-ordered means (FCOM) clustering algorithm for scatter domain partition to identify premises of fuzzy rules. The clustering algorithm elaborates typicality of each object. Data items with low typicalities are removed from further analysis. The paper is accompanied by experiments that show the efficacy of our modified neuro-fuzzy system to identify fuzzy models robust to high ratios of outliers.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2021, 31, 2; 303-319
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Fault detection and isolation with robust principal component analysis
Autorzy:: Tharrault, Y.
Mourot, G.
Ragot, J.
Maquin, D.
Powiązania:: https://bibliotekanauki.pl/articles/929927.pdf
Data publikacji:: 2008
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: analiza głównych składowych
odporność
detekcja uszkodzeń
lokalizacja uszkodzeń
principal component analysis
robustness
outliers
fault detection
fault isolation
structured residual vector
variable reconstruction
Opis:: Principal component analysis (PCA) is a powerful fault detection and isolation method. However, the classical PCA, which is based on the estimation of the sample mean and covariance matrix of the data, is very sensitive to outliers in the training data set. Usually robust principal component analysis is applied to remove the effect of outliers on the PCA model. In this paper, a fast two-step algorithm is proposed. First, the objective was to find an accurate estimate of the covariance matrix of the data so that a PCA model might be developed that could then be used for fault detection and isolation. A very simple estimate derived from a one-step weighted variance-covariance estimate is used (Ruiz-Gazen, 1996). This is a 'local' matrix of variance which tends to emphasize the contribution of close observations in comparison with distant observations (outliers). Second, structured residuals are used for multiple fault detection and isolation. These structured residuals are based on the reconstruction principle, and the existence condition of such residuals is used to determine the detectable faults and the isolable faults. The proposed scheme avoids the combinatorial explosion of faulty scenarios related to multiple faults to be considered. Then, this procedure for outliers detection and isolation is successfully applied to an example with multiple faults.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2008, 18, 4; 429-442
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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