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An Algorithm for Setting Sugeno-Type Fuzzy Inference Systems

An Algorithm for Setting Sugeno-Type Fuzzy Inference Systems An algorithm for tuning fuzzy inference systems of the Sugeno type of zero order based on statistical data is presented, which assumes the allocation of a set of reference points in the space of input variables, where the values of the output variable are calculated using linear regression. The results of the algorithm are presented, showing the effectiveness of its application for the synthesis of intelligent information systems for various purposes. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automatic Documentation and Mathematical Linguistics Springer Journals

An Algorithm for Setting Sugeno-Type Fuzzy Inference Systems

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Publisher
Springer Journals
Copyright
Copyright © Allerton Press, Inc. 2021. ISSN 0005-1055, Automatic Documentation and Mathematical Linguistics, 2021, Vol. 55, No. 3, pp. 79–88. © Allerton Press, Inc., 2021. Russian Text © The Author(s), 2021, published in Nauchno-Tekhnicheskaya Informatsiya, Seriya 2: Informatsionnye Protsessy i Sistemy, 2021, No. 5, pp. 1–11.
ISSN
0005-1055
eISSN
1934-8371
DOI
10.3103/s000510552103002x
Publisher site
See Article on Publisher Site

Abstract

An algorithm for tuning fuzzy inference systems of the Sugeno type of zero order based on statistical data is presented, which assumes the allocation of a set of reference points in the space of input variables, where the values of the output variable are calculated using linear regression. The results of the algorithm are presented, showing the effectiveness of its application for the synthesis of intelligent information systems for various purposes.

Journal

Automatic Documentation and Mathematical LinguisticsSpringer Journals

Published: May 1, 2021

Keywords: intelligent information system; fuzzy inference system; fuzzy inference system tuning; Sugeno-type system; linear regression; membership function

References