Autor: Markowska-Kaczmar, U. - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: An evolutionary algorithm determining a defuzzyfication functional
Autorzy:: Kosiński, W.
Markowska-Kaczmar, U.
Powiązania:: https://bibliotekanauki.pl/articles/1943273.pdf
Data publikacji:: 2007
Wydawca:: Politechnika Gdańska
Tematy:: ordered fuzzy numbers
defuzzyfication
genetic algorithm
Opis:: Order fuzzy numbers are defined that make it possible to deal with fuzzy inputs quantitatively, exactly in the same way as with real numbers, together with four algebraic operations. An approximation formula is given for a defuzzyfication functional that plays the main role when dealing with fuzzy controllers and fuzzy inference systems. A dedicated evolutionary algorithm is presented in order to determine the form of a functional when a training set is given. The form of a genotype composed of three types of chromosomes and the fitness function are given and Genetic operators are proposed.
Źródło:: TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2007, 11, 1-2; 47-58
1428-6394
Pojawia się w:: TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: Evolutionary approach to rule extraction from medical data
Autorzy:: Kwaśnicka, H.
Markowska-Kaczmar, U.
Osojca, T.
Powiązania:: https://bibliotekanauki.pl/articles/333717.pdf
Data publikacji:: 2004
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: ekstrakcja reguł
obliczenia ewolucyjne
rule extraction
evolutionary computation
Opis:: In the paper the method called CGA based on a cooperating genetic algorithm is presented. The CGA is developed for searching a set of rules describing classes in classification problems on the basis of training examples. The details of the method, such as a schema of coding (a chromosome), and a fitness function are shortly described. The method is independent of the type of attributes and it allows choosing different evaluation functions. Developed method was tested using different benchmark data sets. Next, in order to evaluate the efficiency of CGA, it was tested using the Breast Cancer data set with 10 fold cross validation technique.
Źródło:: Journal of Medical Informatics & Technologies; 2004, 7; KB3-12
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

Artykuł

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