Interval-based evolving modeling

Daniel F. Leite, Pyramo Costa, Fernando Gomide

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9 Citas (Scopus)

Resumen

This paper introduces a granular, interval-based evolving modeling (IBeM) approach to develop system models from a stream of data. IBeM is an evolving rule-based modeling scheme that gradually adapts its structure (information granules and rule base) and rules antecedent and consequent parameters from data (inductive learning). Its main purpose is continuous learning, self-organization, and adaptation to unknown environments. The IBeM approach develops global model of a system using a fast, one-pass learning algorithm, and modest memory requirements. To illustrate the effectiveness of the approach, the paper considers actual time series forecasting applications concerning electricity load and stream flow forecasting.

Idioma originalInglés
Título de la publicación alojada2009 IEEE Workshop on Evolving and Self-Developing Intelligent Systems, ESDIS 2009 - Proceedings
Páginas1-8
Número de páginas8
DOI
EstadoPublicada - 2009
Publicado de forma externa
Evento2009 IEEE Workshop on Evolving and Self-Developing Intelligent Systems, ESDIS 2009 - Nashville, TN, Estados Unidos
Duración: 30 mar. 20092 abr. 2009

Serie de la publicación

Nombre2009 IEEE Workshop on Evolving and Self-Developing Intelligent Systems, ESDIS 2009 - Proceedings

Conferencia

Conferencia2009 IEEE Workshop on Evolving and Self-Developing Intelligent Systems, ESDIS 2009
País/TerritorioEstados Unidos
CiudadNashville, TN
Período30/03/092/04/09

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