Data Driven Fuzzy Modeling Using Level Sets

Daniel Leite, Fernando Gomide, Ronald Yager

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

2 Scopus citations

Abstract

The paper looks at the structure of fuzzy rule-based models from the point of view of a function relating membership grades of inputs with rule outputs. This view in turn is generalized by an approach that produces the output functions of the fuzzy rules using input and output data. In this view, a formulation to compute the output of the model consists of estimating the parameters of the output functions. Essentially, the paper suggests an alternative method for fuzzy modeling based on output functions constructed from level sets and input and output data. The data driven method provides an easy and efficient way to develop and process fuzzy models. Examples of function estimation problems show that the data driven level set method is very effective when compared with alternative modeling techniques.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Fuzzy Systems, FUZZ 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665467100
DOIs
StatePublished - 2022
Externally publishedYes
Event2022 IEEE International Conference on Fuzzy Systems, FUZZ 2022 - Padua, Italy
Duration: 18 Jul 202223 Jul 2022

Publication series

NameIEEE International Conference on Fuzzy Systems
Volume2022-July
ISSN (Print)1098-7584

Conference

Conference2022 IEEE International Conference on Fuzzy Systems, FUZZ 2022
Country/TerritoryItaly
CityPadua
Period18/07/2223/07/22

Keywords

  • data driven modeling
  • fuzzy modeling

Fingerprint

Dive into the research topics of 'Data Driven Fuzzy Modeling Using Level Sets'. Together they form a unique fingerprint.

Cite this