On the issue of learning weights from observations for fuzzy signatures

B. Sumudu U. Mendis, Tamás D. Gedeon, Lâszló T. Kóczy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

We investigate the issue of obtaining weights, which are associated with aggregation in fuzzy signatures, from real world data, Our approach will provide a way to extract the relevance of lower levels to the higher levels of the hierarchical fuzzy signature structure. We also handle the non-differentiability of max-min aggregation functions for gradient based learning. A mathematically proved method, which is found in the literature to approximate the derivatives of max-min functions, has been used. Copyright - World Automation Congress (WAC) 2006.

Original languageEnglish
Title of host publication2006 World Automation Congress, WAC'06
PublisherIEEE Computer Society
ISBN (Print)1889335339, 9781889335339
DOIs
Publication statusPublished - Jan 1 2006
Event2006 World Automation Congress, WAC'06 - Budapest, Hungary
Duration: Jun 24 2006Jun 26 2006

Publication series

Name2006 World Automation Congress, WAC'06

Other

Other2006 World Automation Congress, WAC'06
CountryHungary
CityBudapest
Period6/24/066/26/06

Keywords

  • Fuzzy signatures
  • Vector valued fuzzy sets
  • Weighted aggregation

ASJC Scopus subject areas

  • Control and Systems Engineering

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    Mendis, B. S. U., Gedeon, T. D., & Kóczy, L. T. (2006). On the issue of learning weights from observations for fuzzy signatures. In 2006 World Automation Congress, WAC'06 [4259974] (2006 World Automation Congress, WAC'06). IEEE Computer Society. https://doi.org/10.1109/WAC.2006.376058