Constrained voting extreme learning machine and its application

• 作者: MIN Mengcan CHEN Xiaofang XIE Yongfang
• 来源期刊: 系统工程与电子技术（英文版）
• 年，卷（期）: 2021,（1）
• 所属期刊栏目: CONTROL THEORY AND APPLICATION
• 页码范围: 209-219
• 页数: 11页
• 语种: 英文
• DOI: 10.23919/JSEE.2021.000018

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 in-crease 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 pro-posed method, the voting selection is introduced. The proposed method is evaluated on public benchmark datasets. The experi-mental 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 reaches 87.4％, and the experimental results demonstrate that the pro-posed method is more robust than the existing state-of-the-art identification methods.