Online Sequential Double Parallel Extreme Learning Machine for Classifications

作     者：Mingchen YAO Chao ZHANG Wei WU 

作者机构：School of Mathematical SciencesDalian University of Technology 

出 版 物：《Journal of Mathematical Research with Applications》 (数学研究及应用(英文))

年 卷 期：2016年第36卷第5期

页      面：621-630页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 081201[工学-计算机系统结构] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by the National Natural Science Foundation of China(Grant Nos.11401076 61473328 11171367 61473059) 

主　　题：double parallel forward neural network perception extreme learning machine classification problems 

摘      要：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 *** 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.