Fuzzy system identification via probabilistic sets

Kaoru Hirota; Witold Pedrycz

doi:10.1016/0020-0255(82)90030-5

Fuzzy system identification via probabilistic sets

Kaoru Hirota
, Witold Pedrycz

Katedra Pomiarów i Systemów Sterowania (RAU/RAU2)

Hosei University

Wyniki badań: Wkład do czasopisma › Artykuł › recenzja

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Abstrakt

The paper deals with the applications of probabilistic sets in system theory, especially in system identification and the design of fuzzy logic controllers. Fuzzy systems described by means of fuzzy relational equations and Λ-fuzzy systems are discussed. The identification procedure is based on some ideas of clustering techniques and probabilistic sets. Numerical examples using fuzzy and nonfuzzy data are used to illustrate the theory.

Język oryginału	angielski
Strony (od–do)	21-43
Liczba stron	23
Czasopismo	Information Sciences
Tom	28
Numer wydania	1
Identyfikatory DOI	https://doi.org/10.1016/0020-0255(82)90030-5
Status publikacji	Opublikowano - paź 1982

Obszary tematyczne ASJC Scopus

Oprogramowanie
Inżynieria sterowania i systemów
Informatyka teoretyczna
Zastosowania informatyki
Systemy informacyjne i zarządzanie
Sztuczna inteligencja

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10.1016/0020-0255(82)90030-5

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Cytowanie

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title = "Fuzzy system identification via probabilistic sets",

abstract = "The paper deals with the applications of probabilistic sets in system theory, especially in system identification and the design of fuzzy logic controllers. Fuzzy systems described by means of fuzzy relational equations and Λ-fuzzy systems are discussed. The identification procedure is based on some ideas of clustering techniques and probabilistic sets. Numerical examples using fuzzy and nonfuzzy data are used to illustrate the theory.",

author = "Kaoru Hirota and Witold Pedrycz",

year = "1982",

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AB - The paper deals with the applications of probabilistic sets in system theory, especially in system identification and the design of fuzzy logic controllers. Fuzzy systems described by means of fuzzy relational equations and Λ-fuzzy systems are discussed. The identification procedure is based on some ideas of clustering techniques and probabilistic sets. Numerical examples using fuzzy and nonfuzzy data are used to illustrate the theory.

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U2 - 10.1016/0020-0255(82)90030-5

DO - 10.1016/0020-0255(82)90030-5

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AN - SCOPUS:0020190250

SN - 0020-0255

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JO - Information Sciences

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IS - 1

ER -

Fuzzy system identification via probabilistic sets

Abstrakt

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