北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2007, Vol. 30 ›› Issue (1): 123-126.doi: 10.13190/jbupt.200701.123.gey

• 研究报告 • 上一篇    下一篇

基于模糊神经网络的CDMA网络故障诊断方法

葛 艳1, 王 薇2, 闫传军3, 吴 鹏1, 任志考1   

  1. 1.青岛科技大学 信息科学技术学院, 青岛 266061; 2. 青岛科技大学 自动化学院, 青岛 266061; 3.中国联通青岛分公司, 青岛266071
  • 收稿日期:2006-02-28 修回日期:1900-01-01 出版日期:2007-03-30 发布日期:2007-03-30
  • 通讯作者: 葛 艳

A Fuzzy Neural Network Based Fault Diagnosis Method for the CDMA Network

GE Yan1, WANG Wei 2, YAN Chuan-jun 3, WU Peng1, REN Zhi-kao1   

  1. 1. Institute of information science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China;
    2. Institute of Automation, Qingdao University of Science and Technology, Qingdao, 266061, China;
    3. China United Telecommunications Corporation Qingdao Branch, Qingdao, 266071, China
  • Received:2006-02-28 Revised:1900-01-01 Online:2007-03-30 Published:2007-03-30
  • Contact: GE Yan1

摘要:

为解决常规故障诊断算法难以对CDMA网络故障建模的难题,提出了基于模糊神经网络的CDMA网络故障诊断模型. 该模型的输出层和输入层神经元的个数分别由CDMA网络故障类型和故障诊断所需要的输入量决定. 然后再利用故障诊断专家知识库中的故障诊断样本,对诊断模型进行训练,确定网络的连接权值和模糊隶属度函数. 仿真实验结果验证了该故障诊断模型的有效性.

关键词: 故障诊断, 模糊神经网络, CDMA网络, 网络优化

Abstract:

A fuzzy neural network (FNN) based fault diagnosis model for the code division multiple access (CDMA) network is proposed which focusing on solving the difficult problem that general diagnosis algorithms can hardly build model for the CDMA network fault diagnosis system. For the proposed fault diagnosis model, the numbers of neurons in the output layer are based on the fault types of the CDMA network, and the amount of input variables needed for those fault diagnosis determine the number of neurons in the input layer. Moreover, the synaptic weighting value and fuzzy member function are obtained from training the model with the input/output fault diagnosis data in the expert knowledge base. Computer simulation results show the effectiveness and the applicability of the proposed method.

Key words: fault diagnosis, fuzzy-neural network, code division multiple access network, network optimization

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