...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Aerospace science and technology >Retargeting extreme learning machines for classification and their applications to fault diagnosis of aircraft engine
【24h】

Retargeting extreme learning machines for classification and their applications to fault diagnosis of aircraft engine

机译：重新定位用于分类的极限学习机及其在飞机发动机故障诊断中的应用

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Since the original extreme learning machine (ELM) generates the hidden nodes randomly, it usually needs more hidden nodes to reach the good classification performance. However, more hidden nodes will jeopardize the real time, which limits its applications to the testing time sensitive scenarios. To this end, the commonly-used methods tend to compact its structure via optimizing the number of hidden nodes. Different from this viewpoint of network structure, in this paper two algorithms are proposed to improve the real time performance of ELM from a viewpoint of data structure. Specially, they improve the ELM classification performance by retargeting its label vectors. As thus, they need fewer hidden nodes to reach the same classification performance, which means the better real time. Finally, experimental results on the benchmark data sets validate the effectiveness and feasibility of the presented two algorithms. To be more important, they are applied to the fault diagnosis of aircraft engine and can be developed as its candidate techniques. (C) 2017 Elsevier Masson SAS. All rights reserved.
机译：由于原始的极限学习机（ELM）会随机生成隐藏节点，因此通常需要更多的隐藏节点才能达到良好的分类性能。但是，更多的隐藏节点将危害实时性，这将其应用程序限制在对时间敏感的测试场景中。为此，常用的方法倾向于通过优化隐藏节点的数量来压缩其结构。从网络结构的角度出发，本文从数据结构的角度提出了两种算法来提高ELM的实时性能。特别地，它们通过重新定位其标记向量来提高ELM分类性能。因此，他们需要更少的隐藏节点来达到相同的分类性能，这意味着更好的实时性。最后，在基准数据集上的实验结果验证了所提出的两种算法的有效性和可行性。更重要的是，它们被应用到飞机发动机的故障诊断中，并且可以作为其候选技术而发展。 （C）2017 Elsevier Masson SAS。版权所有。

著录项

  • 来源
    《Aerospace science and technology》 |2017年第12期|603-618|共16页
  • 作者

    Zhao Yong-Ping; Song Fang-Quan; Pan Ying-Ting; Li Bing;

  • 作者单位

    Nanjing Univ Aeronaut & Astronaut, Coll Energy & Power Engn, Nanjing 210016, Jiangsu, Peoples R China;

    Nanjing Univ Aeronaut & Astronaut, Coll Energy & Power Engn, Nanjing 210016, Jiangsu, Peoples R China;

    Nanjing Univ Aeronaut & Astronaut, Coll Energy & Power Engn, Nanjing 210016, Jiangsu, Peoples R China;

    Nanjing Univ Aeronaut & Astronaut, Coll Energy & Power Engn, Nanjing 210016, Jiangsu, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Extreme learning machine; Machine learning algorithm; Fault diagnosis; Aircraft engine;

    机译：极限学习机;机器学习算法;故障诊断;飞机发动机;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号