Partitioned online sequential extreme learning machine for large ordered system modeling

Authors:
Junseok Lim
Affiliations:
Department of Electronics Engineering, Sejong University, 98 Kwangjin Kunja, Seoul 143-747, Republic of Korea
Venue:
Neurocomputing
Year:
2013

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Abstract

In this paper, we propose an algorithm entitled ''partitioned OS-ELM'' (POS-ELM) that partitions a large data matrix into small matrices, applies an RLS (Recursive Least Square) scheme in each of the small sub-matrices and assembles the whole estimation vector by the concatenation of the sub-vectors from the RLS outputs of the sub-matrices. Consequently, the algorithm is less complex than the conventional OS-ELM and maintains an almost compatible estimation performance.