A Novel Robust Online Extreme Learning Machine for the Non-Gaussian Noise  

作　　者：GU Jun ZOU Quanyi DENG Changhui WANG Xiaojun 

机构地区：[1]College of Information Engineering,Dalian Ocean University,Dalian 116023,China [2]School of software Engineering,South China University of Technology,Guang Zhou 510006,China [3]School of Management Science and Engineering,Dongbei University of Finance and Economics,Dalian 116025,China

出　　处：《Chinese Journal of Electronics》2023年第1期130-139,共10页电子学报（英文版）

基　　金：supported by Scientific Research Funding Project of the Education Department of Liaoning Province (LN2019Q44)。

摘　　要：Samples collected from most industrial processes have two challenges:one is contaminated by the non-Gaussian noise,and the other is gradually obsolesced.This feature can obviously reduce the accuracy and generalization of models.To handle these challenges,a novel method,named the robust online extreme learning machine(RO-ELM),is proposed in this paper,in which the least mean p-power criterion is employed as the cost function which is to boost the robustness of the ELM,and the forgetting mechanism is introduced to discard the obsolescence samples.To investigate the performance of the ROELM,experiments on artificial and real-world datasets with the non-Gaussian noise are performed,and the datasets are from regression or classification problems.Results show that the RO-ELM is more robust than the ELM,the online sequential ELM(OS-ELM) and the OSELM with forgetting mechanism(FOS-ELM).The accuracy and generalization of the RO-ELM models are better than those of other models for online learning.

关 键 词：Extreme learning machine(ELM) Online learning Non-Gaussian noise Obsolescence samples Least mean p-power(LMP) 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]