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基于模糊神经网络的CDMA网络故障诊断方法
引用本文:葛艳,王薇,闫传军,吴鹏,任志考.基于模糊神经网络的CDMA网络故障诊断方法[J].北京邮电大学学报,2007,29(1):123-126.
作者姓名:葛艳  王薇  闫传军  吴鹏  任志考
作者单位: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
基金项目:青岛科技大学校科研和教改项目
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
文章编号:1007-5321(2007)01-0123-04
收稿时间:2006-02-28
修稿时间:2006年2月28日

A Fuzzy Neural Network Based Fault Diagnosis Method for the CDMA Network
GE Yan,WANG Wei,YAN Chuan-jun,WU Peng,REN Zhi-kao.A Fuzzy Neural Network Based Fault Diagnosis Method for the CDMA Network[J].Journal of Beijing University of Posts and Telecommunications,2007,29(1):123-126.
Authors:GE Yan  WANG Wei  YAN Chuan-jun  WU Peng  REN Zhi-kao
Affiliation: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
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.
Keywords:fault diagnosis  fuzzy-neural network  code division multiple access network  network optimization
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