A Note on the KM Algorithm for Computing the Variance of an Interval Type-2 Fuzzy Set

Juan Carlos Figueroa-García, Carlos Franco, Jhoan Sebastian Tenjo-García

Research output: Chapter in Book/ReportChapter

Abstract

This work presents some considerations about the computation of the relative variance for Interval Type-2 Fuzzy Sets. Based on experimental evidence, the computation of the relative variance of an interval Type-2 fuzzy set using the KM algorithm needs more computations than using the original KM algorithm in order to obtain their boundaries since the variance combined with the KM algorithm results in a nonlinear behavior. A modified KM algorithm for computing the relative variance of an interval type-2 fuzzy set is proposed and a comprehensive analysis of its behavior is provided through two examples.

Original languageEnglish (US)
Title of host publicationExplainable AI and Other Applications of Fuzzy Techniques - Proceedings of the 2021 Annual Conference of the North American Fuzzy Information Processing Society, NAFIPS 2021
EditorsJulia Rayz, Victor Raskin, Scott Dick, Vladik Kreinovich
PublisherSpringer Science and Business Media Deutschland GmbH
Pages130-140
Number of pages11
ISBN (Electronic)978-3-030-82099-2
ISBN (Print)9783030820985
DOIs
StatePublished - 2022
EventAnnual Conference of the North American Fuzzy Information Processing Society, NAFIPS 2021 - West Lafayette, United States
Duration: Jun 7 2021Jun 9 2021

Publication series

NameLecture Notes in Networks and Systems
Volume258
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceAnnual Conference of the North American Fuzzy Information Processing Society, NAFIPS 2021
Country/TerritoryUnited States
CityWest Lafayette
Period6/7/216/9/21

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

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