• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Prediction of Flight Status of Logistics UAVs Based on an Information Entropy Radial Basis Function Neural Network
【2h】

Prediction of Flight Status of Logistics UAVs Based on an Information Entropy Radial Basis Function Neural Network

机译：基于信息熵径向基础函数神经网络的物流无人机航班地位预测

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Aiming at addressing the problems of short battery life, low payload and unmeasured load ratio of logistics Unmanned Aerial Vehicles (UAVs), the Radial Basis Function (RBF) neural network was trained with the flight data of logistics UAV from the Internet of Things to predict the flight status of logistics UAVs. Under the condition that there are few available input samples and the convergence of RBF neural network is not accurate, a dynamic adjustment method of RBF neural network structure based on information entropy is proposed. This method calculates the information entropy of hidden layer neurons and output layer neurons, and quantifies the output information of hidden layer neurons and the interaction information between hidden layer neurons and output layer neurons. The structural design and optimization of RBF neural network were solved by increasing the hidden layer neurons or disconnecting unnecessary connections, according to the connection strength between neurons. The steepest descent learning algorithm was used to correct the parameters of the network structure to ensure the convergence accuracy of the RBF neural network. By predicting the regression values of the flight status of logistics UAVs, it is demonstrated that the information entropy-based RBF neural network proposed in this paper has good approximation ability for the prediction of nonlinear systems.
机译：旨在解决电池寿命短，低有效载荷和未测量的物流空中飞行器（无人机）的载荷比的问题，径向基函数（RBF）神经网络从事物流UAV的飞行数据培训到预测物流无人机的航班地位。在只有可用输入样本的条件下，提出了基于信息熵的RBF神经网络结构的动态调整方法。此方法计算隐藏层的神经元和输出层的神经元的信息熵，并量化隐藏层神经元的输出的信息和隐藏层的神经元和输出层的神经元之间的相互作用的信息。根据神经元之间的连接强度，通过增加隐藏层神经元或断开不必要的连接来解决RBF神经网络的结构设计和优化。最陡的下降学习算法用于校正网络结构的参数，以确保RBF神经网络的收敛准确性。通过预测的物流无人机的飞行状态回归价值，它证明了本文提出的基于信息熵RBF神经网络具有的非线性系统的预测良好的逼近能力。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

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