分类问题的在线顺序双并联快速学习机

双并联前馈神经网络模型是单层感知机和单隐层前馈神经网络的混合结构,本文构造了一种双并联快速学习机算法,与其他类似算法比较,提出的算法能利用较少的隐层单元及更少的待定参数,获得近似的学习性能.数值实验表明,对很多实际分类问题,提出的算法具备更佳的泛化能力,因而可以作为快速学习机算法的有益补充.

Online Sequential Double Parallel Extreme Learning Machine for Classifications

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.