Interval-based evolving modeling

Daniel F. Leite, Pyramo Costa, Fernando Gomide

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

9 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2009 IEEE Workshop on Evolving and Self-Developing Intelligent Systems, ESDIS 2009 - Proceedings
Pages1-8
Number of pages8
DOIs
StatePublished - 2009
Externally publishedYes
Event2009 IEEE Workshop on Evolving and Self-Developing Intelligent Systems, ESDIS 2009 - Nashville, TN, United States
Duration: 30 Mar 20092 Apr 2009

Publication series

Name2009 IEEE Workshop on Evolving and Self-Developing Intelligent Systems, ESDIS 2009 - Proceedings

Conference

Conference2009 IEEE Workshop on Evolving and Self-Developing Intelligent Systems, ESDIS 2009
Country/TerritoryUnited States
CityNashville, TN
Period30/03/092/04/09

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