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

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

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.

Original language	English
Pages (from-to)	21-43
Number of pages	23
Journal	Information Sciences
Volume	28
Issue number	1
DOIs	https://doi.org/10.1016/0020-0255(82)90030-5
Publication status	Published - Oct 1982
Externally published	Yes

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10.1016/0020-0255(82)90030-5

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Hirota, K., & Pedrycz, W. (1982). Fuzzy system identification via probabilistic sets. Information Sciences, 28(1), 21-43. https://doi.org/10.1016/0020-0255(82)90030-5

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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",

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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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Fuzzy system identification via probabilistic sets

Abstract

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