Constrained voting extreme learning machine and its application  

作　　者：MIN Mengcan CHEN Xiaofang XIE Yongfang 

机构地区：[1]School of Automation,Central South University,Changsha 410083,China

出　　处：《Journal of Systems Engineering and Electronics》2021年第1期209-219,共11页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(61773405;61751312);the Major Scientific and Technological Innovation Projects of Shandong Province(2019JZZY020123)。

摘　　要：Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.With the increase of the nodes in the hidden layers,the computation cost is greatly increased.In this paper,we propose a novel algorithm,named constrained voting extreme learning machine(CV-ELM).Compared with the traditional ELM,the CV-ELM determines the input weight and bias based on the differences of between-class samples.At the same time,to improve the accuracy of the proposed method,the voting selection is introduced.The proposed method is evaluated on public benchmark datasets.The experimental results show that the proposed algorithm is superior to the original ELM algorithm.Further,we apply the CV-ELM to the classification of superheat degree(SD)state in the aluminum electrolysis industry,and the recognition accuracy rate reaches87.4%,and the experimental results demonstrate that the proposed method is more robust than the existing state-of-the-art identification methods.

关 键 词：extreme learning machine(ELM) majority voting ensemble method sample based learning superheat degree(SD) 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]