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IJON
2016

An adaptive ensemble of on-line Extreme Learning Machines with variable forgetting factor for dynamic system prediction

3 years 12 months ago
An adaptive ensemble of on-line Extreme Learning Machines with variable forgetting factor for dynamic system prediction
A demand for predictive models for on-line estimation of variables is increasing in industry. As industrial processes are timevarying, on-line learning algorithms should be adaptive to capture process changes. On-line ensemble methods have been shown to provide better generalization performance than single models in changing environments. However, most on-line ensembles do not include and exclude models during on-line operation. As a result, the ensembles have limited adaptation capability. Moreover, a higher performance can be obtained by combining a selected set of most relevant models of the ensemble for the current situation, rather than combining all the models. This paper proposes a new on-line learning ensemble of regressor models using an ordered aggregation (OA) technique which is able to provide on-line predictions of variables in changing environments. OA dynamically selects an optimal size and composition of a subset of models based on the minimization of the ensemble erro...
Symone G. Soares, Rui Araújo
Added 05 Apr 2016
Updated 05 Apr 2016
Type Journal
Year 2016
Where IJON
Authors Symone G. Soares, Rui Araújo
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