Partitioned online sequential extreme learning machine for large ordered system modeling

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.