Data Driven Fuzzy Modeling Using Level Sets

Daniel Leite, Fernando Gomide, Ronald Yager

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

3 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojada2022 IEEE International Conference on Fuzzy Systems, FUZZ 2022 - Proceedings
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781665467100
DOI
EstadoPublicada - 2022
Publicado de forma externa
Evento2022 IEEE International Conference on Fuzzy Systems, FUZZ 2022 - Padua, Italia
Duración: 18 jul. 202223 jul. 2022

Serie de la publicación

NombreIEEE International Conference on Fuzzy Systems
Volumen2022-July
ISSN (versión impresa)1098-7584

Conferencia

Conferencia2022 IEEE International Conference on Fuzzy Systems, FUZZ 2022
País/TerritorioItalia
CiudadPadua
Período18/07/2223/07/22

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