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Bayes classification of imprecise information of interval type

Kulczycki P., Kowalski P. A.

Control and Cybernetics

2011

Vol. 40, no 1

101-123

The subject of the investigation presented here is Bayes classification of imprecise multidimensional information of interval type by means of patterns defined through precise data, e.g. deterministic or sharp. For this purpose the statistical kernel estimators methodology was applied, which makes the resulting algorithm independent of the pattern shape. In addition, elements of pattern sets which have insignificant or negative influence on the correctness of classification are eliminated. The concept for realizing the procedure is based on the sensitivity method, used in the domain of artificial neural networks. As a result of this procedure the number of correct classifications and - above all - calculation speed increased significantly. A further growth in quality of classification was achieved with an algorithm for the correction of classifier parameter values. The results of numerical verification, carried out on pseudorandom and benchmark data, as well as a comparative analysis with other methods of similar conditioning, have validated the concept presented here and its positive features.

Identyfikacja rozkładu w systemach rzeczywistych za pomocą estymatorów jądrowych

Łukasik S.

Czasopismo Techniczne. Elektrotechnika

2008

R. 105, z. 1-E

3-13

Metody nieparametryczne znajdują coraz szersze zastosowanie w zagadnieniach współczesnej analizy i eksploracji danych. W artykule przedstawiono najpopularniejsze narzędzie powyższych metod - estymatory jądrowe. Poza koncepcją tychże estymatorów zaprezentowano także praktyczne aspekty estymacji oraz przykłady jej zastosowań do wyznaczania rozkładów z dziedziny fizyki wysokich energii, wykrywania uszkodzeń silnika asynchronicznego oraz analizy danych socjologicznych.

Nonparametric methods find increasing number of applications in the area of data analysis and data exploration. In this paper the most popular tool of those methods was presented - kernel estimators. Beside of kernel estimators' concept, practical estimation aspects and examples of applications in determining distributions from nuclear physics experiments, induction motors' damage detection and analysis of sociological data were shown as well.

On the application of statistical kernel estimators for the demand-based design of a wireless data transmission system

Kulczycki P., Waglowski J.

Control and Cybernetics

2005

Vol. 34, no 4

1149-1167

The subject of this paper is the task of designing the LMDS (Local Multipoint Distribution System) wireless broadband data transmission system. The methodology of statistical kernel estimators and fuzzy logic using operations research and mathematical programming is applied to find optimal locations for its basestations. A procedure which allows to obtain such locations on the basis of potential customer distribution and their expected demand, also in the cases of uncertain and non-stationary data, is investigated.