...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Computers >A BP Neural Network Realization in the Measurement of Material Permittivity
【24h】

A BP Neural Network Realization in the Measurement of Material Permittivity

机译：介电常数测量中的BP神经网络实现

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Effective complex permittivity measurements of materials are important in microwave engineering and microwave chemistry. The BP (Back Propagation) neural network computational module has been applied to microwave technology and becomes a useful tool recently. A neural network can be trained to learn the behavior of an effective complex permittivity of material under microwave irradiation in an experimental system. It can provide a fast and accurate result for the material permittivity. Thus, the on-line measurement has been realized. In this paper, a measurement system has been designed and the S-parameters are obtained by full-wave simulations to reconstruct the material permittivity. Moreover, several organic solvents have been measured. The relative errors of the reconstructed results for several organic solvents are less than 5% compared with reference data. The reconstructed results of the effective permittivities of solvents by means of the BP neural network are obtained quickly and accurately.
机译：有效的材料复介电常数测量在微波工程和微波化学中很重要。 BP（反向传播）神经网络计算模块已应用于微波技术，并且最近成为有用的工具。可以训练神经网络来学习在实验系统中微波辐照下材料有效复介电常数的行为。它可以为材料的介电常数提供快速而准确的结果。因此，已经实现了在线测量。本文设计了一种测量系统，并通过全波模拟获得了S参数，以重建材料的介电常数。此外，已经测量了几种有机溶剂。与参考数据相比，几种有机溶剂的重建结果的相对误差小于5％。借助BP神经网络可以快速，准确地获得溶剂有效介电常数的重建结果。

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