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 reaches 87.4%, and the experimental results demonstrate that the proposed method is more robust than the existing state-of-the-art identification methods.

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约束投票极限学习机及其应用

研究人员已证明极限学习机（ELM）是一种有效的模式分类和回归学习机制。但是，其良好的性能基于大量的隐藏层节点。随着隐藏层中节点的增加，计算成本大大增加。在本文中，我们提出了一种新颖的算法，称为约束投票极限学习机（CV-ELM）。与传统的ELM相比，CV-ELM根据类之间的差异确定输入权重和偏差。同时，为了提高所提方法的准确性，引入了投票选择方法。所提出的方法在公共基准数据集上进行了评估。实验结果表明，该算法优于原ELM算法。进一步，