[1]石会鹏,潘冀,刘海洋,等.采用径向基神经网络的卫星网络申报趋势分析方法[J].华侨大学学报(自然科学版),2021,42(2):268-274.[doi:10.11830/ISSN.1000-5013.202001014]
　SHI Huipeng,PAN Ji,LIU Haiyang,et al.Analysis Method of Satellite Network Declaration Trend Using Radial Basis Function Neural Network[J].Journal of Huaqiao University(Natural Science),2021,42(2):268-274.[doi:10.11830/ISSN.1000-5013.202001014]
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采用径向基神经网络的卫星网络申报趋势分析方法()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第42卷
期数:
2021年第2期
页码:
268-274
栏目:
出版日期:
2021-03-20

文章信息/Info

Title:
Analysis Method of Satellite Network Declaration Trend Using Radial Basis Function Neural Network
文章编号:
1000-5013(2021)02-0268-07
作者:
石会鹏1;  潘冀1;  刘海洋1;  赵睿2;  刘珊杉1;  韩锐1
1. 国家无线电监测中心, 北京 100037;2. 华侨大学 信息科学与工程学院, 福建 厦门 361021
Author(s):
SHI Huipeng1;  PAN Ji1;  LIU Haiyang1;  ZHAO Rui2; LIU Shanshan1;  HAN Rui1
1. State Radio Monitoring Center, Beijing 100037, China; 2. College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China
关键词:
卫星网络;  趋势预测;  径向基函数神经网络;  量化分析;  频谱管理
Keywords:
satellite network;  trend forecasting;  radial basis function neural network;  quantitative analysis;  spectrum management
分类号:
TP399
DOI:
10.11830/ISSN.1000-5013.202001014
文献标志码:
A
摘要:
将径向基函数(RBF)神经网络应用于卫星网络申报趋势分析,构建基于RBF神经网络的趋势量化分析方法,改变当前主要依赖专家经验分析申报趋势的现状,为卫星网络申报趋势的评估提供量化指标.首先,梳理当前卫星网络申报的业务特点;然后,对主流预测方法进行分析,提出基于RBF神经网络的申报趋势分析方法;最后,通过实际申报数据进行算法验证.结果表明:文中方法对卫星网络申报趋势的预测误差总体小于20%,对实际申报工作具有指导意义.
Abstract:
Radial basis function(RBF)neural network is innovatively applied to the trend analysis of satellite network declaration. A quantitative trend analysis method based on RBF neural network is proposed, which provides quantitative indicators and methods for evaluating the trend of satellite network declaration and changes current situation that mainly depends on expert experience to analyze declaration trend. Firstly, the business characteristics of current satellite network declaration are combed. Then, the widely used prediction methods are analyzed, thus trend analysis method of declaration is proposed based on RBF neural network. Finally, the algorithm by actual declaration data is verified. The results show that prediction error of the proposed method is less than 20% for the trend of satellite network declaration, which has practical guiding significance.

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备注/Memo

备注/Memo:
收稿日期: 2020-01-13
通信作者: 韩锐(1984-),高级工程师,博士,主要从事卫星频率轨道资源管理、电磁兼容分析的研究.E-mail:hanrui@srrc.org.cn.
基金项目: 国家自然科学基金资助项目(91738101); 中国科学院复杂航天系统电子信息技术重点实验室开放基金资助项目(N201708)
更新日期/Last Update: 2021-03-20