Random variate generation for discrete fuzzy numbers based on α-cuts

Juan Carlos Figueroa-García, Carlos Franco, Roman Neruda

Producción científica: Capítulo en Libro/InformeContribución a la conferencia

Resumen

Probabilistic based random variate generation is the most popular method for representing randomness into simulation scenarios that could occur in real life. The main idea behind the use of random numbers to replicate the value of a uncertain variable can also be extended to fuzzy sets, including discrete fuzzy sets. This way, it is possible to use random numbers and the α -cut of a discrete fuzzy set to retrieve a random variate alongside its membership degree. To do so, a method for computing discrete fuzzy random variates based on α -cuts is proposed and applied to two examples: a comprehensive and a simulation example.

Idioma originalInglés estadounidense
Título de la publicación alojada2024 IEEE 7th International Conference on Big Data and Artificial Intelligence, BDAI 2024
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas259-264
Número de páginas6
ISBN (versión digital)9798350352009
DOI
EstadoPublicada - 2024
Evento7th IEEE International Conference on Big Data and Artificial Intelligence, BDAI 2024 - Beijing, China
Duración: jul. 5 2024jul. 7 2024

Serie de la publicación

Nombre2024 IEEE 7th International Conference on Big Data and Artificial Intelligence, BDAI 2024

Conferencia

Conferencia7th IEEE International Conference on Big Data and Artificial Intelligence, BDAI 2024
País/TerritorioChina
CiudadBeijing
Período7/5/247/7/24

Áreas temáticas de ASJC Scopus

  • Inteligencia artificial
  • Redes de ordenadores y comunicaciones
  • Informática aplicada
  • Visión artificial y reconocimiento de patrones
  • Sistemas de información
  • Gestión y sistemas de información

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