One simple procedure to find the best approximate solution for fuzzy max-average inverse relation

Yan Kuen Wu*, Sy Ming Guu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Fuzzy relational equations have played an important role in fuzzy modeling and applied to many practical problems. Most theories of fuzzy relational equations based on a premise that the solution set is not empty. However, this is often not the case in practical applications. Recently, Chakraborty et al. presented an efficient algorithm to solve the best approximate solution for the fuzzy relational equations, X ○ A = I, with max-min composition, where I denotes the identity matrix. In this paper, some theoretical results of the fuzzy relational equations with max-average composition are proposed for this particular problem. One simple procedure finds the best approximate solution for the discussed problem. A numerical example is provided to illustrate the procedure.

Original languageEnglish
Title of host publication2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Pages2806-2810
Number of pages5
DOIs
StatePublished - 2010
Externally publishedYes
Event2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 - Qingdao, China
Duration: 11 07 201014 07 2010

Publication series

Name2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Volume6

Conference

Conference2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Country/TerritoryChina
CityQingdao
Period11/07/1014/07/10

Keywords

  • Fuzzy relational equations
  • Max-average composition
  • The best approximate solutionc

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