Online Sequential Double Parallel Extreme Learning Machine for Classifications

Abstract:

      Double parallel forward neural network (DPFNN) model is a mixture structure of single-layer perception and single-hidden-layer forward neural network (SLFN). In this paper, by making use of the idea of online sequential extreme learning machine (OS-ELM) on DPFNN, we derive the online sequential double parallel extreme learning machine algorithm (OS-DPELM). Compared to other similar algorithms, our algorithms can achieve approximate learning performance with fewer numbers of hidden units, as well as the parameters to be determined. The experimental results show that the proposed algorithm has good generalization performance for real world classification problems, and thus can be a necessary and beneficial complement to OS-ELM.