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Aproksymacja liczb rozmytych zachowujaca entropijna miare niespecyficznosci

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  • Heilpern St.
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    Abstract

    Zbiory rozmyte okazaly sie bardzo pomocne w modelowaniu i efektywnym przetwarzaniu nieprecyzyjnych informacji. Czasem zachodzi jednak koniecznosc przyblizenia danego zbioru rozmytego za pomoca zbioru nierozmytego. W tym celu stosuje sie zazwyczaj defuzyfikacje (wyostrzanie), ale metoda ta niestety czesto prowadzi do utraty zbyt wielu cennych informacji. W tym przypadku wskazane byc moze posluzenie sie aproksymacja przedzialowa. W niniejszej pracy ograniczymy sie do najwazniejszej podrodziny zbiorow rozmytych, tzn. do liczb rozmytych. Dla wspomnianej rodziny przedstawiono nowa metode aproksymacji przedzialowej, zachowujaca ilosc informacji, jaka dostarcza przyblizana liczba rozmyta. Dokladniej, wprowadzona zostanie pewna miara informacji, zwana entropijna miara niespecyficznosci, a nastepnie wskazana zostanie metoda aproksymacji przedzialowej liczb rozmytych, zachowujaca te miare informacji.

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    Bibliographic Info

    Article provided by Wroclaw University of Technology, Institute of Organization and Management in its journal Operations Research and Decisions.

    Volume (Year): 4 (2003)
    Issue (Month): ()
    Pages: 6

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    Handle: RePEc:wut:journl:v:4:y:2003:p:6

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    Related research

    Keywords: fuzzy set; approximation; interval approximation of fuzzy numbers; entropy-like nonspecifity;

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