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- W2114010478 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." @default.
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- W2114010478 date "2009-03-01" @default.
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- W2114010478 title "Interval-based evolving modeling" @default.
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- W2114010478 doi "https://doi.org/10.1109/esdis.2009.4938992" @default.
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