Type of Publication

Journal Articles

Date:

3 /

2015

Status

Published

DOI:

10.1016/j.jprocont.2015.01.004

On-line Sequential Extreme Learning Machine Based on Recursive Partial Least Squares

Featured in:

Journal of Process Control

Authors:

Tiago Matias, Francisco Souza, Rui Araújo, Nuno Gonçalves and João P. Barreto

Abstract

This paper proposes the online sequential extreme learning machine algorithm based on the recursive partial least-squares method (OS-ELM-RPLS). It is an improvement to the online sequential extreme learning machine based on recursive least-squares (OS-ELM-RLS) introduced in (Huang et al., 2005 [1]). Like in the batch extreme learning machine (ELM), in OS-ELM-RLS the input weights of a single-hidden layer feedforward neural network (SLFN) are randomly generated, however the output weights are obtained by a recursive least-squares (RLS) solution. However, due to multicollinearities in the columns of the hidden-layer output matrix caused by presence of redundant input variables or by the large number of hidden-layer neurons, the problem of estimation the output weights can become ill-conditioned. In order to circumvent or mitigate such ill-conditioning problem, it is proposed to replace the RLS method by the recursive partial least-squares (RPLS) method. OS-ELM-RPLS was applied and compared with three other methods over three real-world data sets. In all the experiments, the proposed method always exhibits the best prediction performance.

Citation
Tiago Matias, Francisco Souza, Rui Araújo, Nuno Gonçalves and João P. Barreto (2015). On-line sequential extreme learning machine based on recursive partial least squares. Journal of Process Control, 27, 15-21. DOI: 10.1016/j.jprocont.2015.01.004

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