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A Novel Improved ELM Algorithm for a Real Industrial Application

机译：一种用于实际工业应用的新型改进ELM算法

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摘要

It is well known that the feedforward neural networks meet numbers of difficulties in the applications because of its slow learning speed. The extreme learning machine (ELM) is a new single hidden layer feedforward neural network method aiming at improving the training speed. Nowadays ELM algorithm has received wide application with its good generalization performance under fast learning speed. However, there are still several problems needed to be solved in ELM. In this paper, a new improved ELM algorithm named R-ELM is proposed to handle the multicollinear problem appearing in calculation of the ELM algorithm. The proposed algorithm is employed in bearing fault detection using stator current monitoring. Simulative results show that R-ELM algorithm has better stability and generalization performance compared with the original ELM and the other neural network methods.
机译：众所周知，前馈神经网络由于学习速度慢而在应用中遇到许多困难。极限学习机（ELM）是一种旨在提高训练速度的新型单隐层前馈神经网络方法。如今，ELM算法以其快速的学习速度和良好的泛化性能而得到了广泛的应用。但是，ELM中仍然需要解决几个问题。本文提出了一种新的改进的ELM算法R-ELM，以解决ELM算法计算中出现的多重共线性问题。该算法用于定子电流监测的轴承故障检测。仿真结果表明，与原始的ELM和其他神经网络方法相比，R-ELM算法具有更好的稳定性和泛化性能。

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  • 来源
    《Mathematical Problems in Engineering》 |2014年第8期|824765.1-824765.7|共7页
  • 作者

    Hai-Gang Zhang; Sen Zhang; Yi-Xin Yin;

  • 作者单位

    School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China;

    School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China;

    School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China;

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