• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of RF and microwave computer-aided engineering >Neural network structures and training algorithms for RF and microwave applications
【24h】

Neural network structures and training algorithms for RF and microwave applications

机译：用于射频和微波应用的神经网络结构和训练算法

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

Neural networks recently gained attention as fast and flexible vehicles to microwave modeling, simulation, and optimization. After learning and abstracting from microwave data, through a process called training, neural network models are used during microwave design to provide instant answers to the task learned. Appropriate neural network structure and suitable training algorithm are two of the major issues in developing neural network models for microwave applications. Together, they decide amount of training data required, accuracy that could possibly be achieved, and more importantly developmental cost of neural models. A review of the current status of this emerging technology is presented, with emphasis on neural network structures and training algorithms suitable for microwave applications. Present challenges and future directions of the area are discussed.©1999 John Wiley & Sons, Inc. Int J RF and Microwave CAE 9: 216–240, 1999.
机译：神经网络最近作为快速灵活的工具而受到关注，用于微波建模，仿真和优化。在从微波数据中学习并提取出来之后，通过称为训练的过程，在微波设计过程中将使用神经网络模型来为所学任务提供即时答案。适当的神经网络结构和合适的训练算法是开发用于微波应用的神经网络模型的两个主要问题。他们共同决定所需的训练数据量，可能实现的准确性以及更重要的是神经模型的开发成本。本文介绍了该新兴技术的现状，重点是适用于微波应用的神经网络结构和训练算法。讨论了该地区目前的挑战和未来的方向。©1999 John Wiley＆Sons，Inc. Int J RF和Microwave CAE 9：216–240，1999年。

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