...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Key Engineering Materials >Fusion Identification for Wear Particles Based on Dempster-Shafter Evidential Reasoning and Back-Propagation Neural Network
【24h】

Fusion Identification for Wear Particles Based on Dempster-Shafter Evidential Reasoning and Back-Propagation Neural Network

机译：基于DS证据推理和反向传播神经网络的磨粒融合识别。

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

Based on Back-Propagation neural network and Dempster-Shafter evidential reasoning, a fuse classification method for identifying wear particles is putted forward. Firstly, digital wear debris images are dealt with images processing methods. Then from the wear particles images, wear particles characters can be obtained by means of statistical analysis and Fourier analysis. Later, an integrated neural network made of two sub-neural networks based on statistical analysis and Fourier analysis is established, and some typical wear particles features as training samples are provided. After each sub-BP neural network has been trained successfully, the preliminary diagnosis of each sub-neural network is achieved. By using of the dempster-shafter evidential reasoning, the finial fusion diagnosis results are obtained. In the end, a practical example is given to show that the fusion results are more accurate than those with a single method only.
机译：基于反向传播神经网络和Dempster-Shafter证据推理，提出了一种用于识别磨损颗粒的保险丝分类方法。首先，用图像处理方法处理数字磨损碎片图像。然后从磨损颗粒图像中，可以通过统计分析和傅立叶分析获得磨损颗粒特征。随后，基于统计分析和傅立叶分析，建立了由两个亚神经网络组成的集成神经网络，并提供了一些典型的磨损颗粒特征作为训练样本。成功训练了每个子BP神经网络后，就可以对每个子神经网络进行初步诊断。通过运用dempster-shafter的证据推理，获得了最终的融合诊断结果。最后，通过一个实例说明融合结果比仅采用一种方法的融合结果更为准确。

著录项

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