薛善良,杨佩茹,周奚.基于模糊神经网络的WSN无线数据收发单元故障诊断[J].计算机科学,2018,45(5):38-43
基于模糊神经网络的WSN无线数据收发单元故障诊断
WSN Wireless Data Transceiver Unit Fault Diagnosis with Fuzzy Neural Network
投稿时间:2017-03-22  修订日期:2017-05-04
DOI:10.11896/j.issn.1002-137X.2018.05.006
中文关键词:  无线传感器网络,故障诊断,电流模型,模糊神经网络
英文关键词:Wireless sensor network,Fault diagnosis,Current model,Fuzzy neural network
基金项目:
作者单位E-mail
薛善良 南京航空航天大学计算机科学与技术学院 南京211106 xuesl@nuaa.edu.cn 
杨佩茹 南京航空航天大学计算机科学与技术学院 南京211106 330474330@qq.com 
周奚 南京航空航天大学计算机科学与技术学院 南京211106  
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中文摘要:
      在一些无线传感器网络(Wireless Sensor Network,WSN)安全监测系统中,节点长时间传输大量数据,导致无线数据收发单元容易出现功率下降和功率放大器(Power Amplifier,PA)被烧毁的现象,而此类故障的诊断方法一般比较复杂且低效。针对上述问题,在分析WSN单元级故障诊断的基础上,利用无线数据收发单元的电流模型,提出了一种基于模糊神经网络的无线数据收发单元故障诊断方法。首先,根据无线数据收发单元中发射消耗的电流与温度和供电电压的关系,建立电流模型;然后,利用聚类算法确定模糊神经网络模型结构,结合混合学习算法优化模糊规则的前件参数和后件参数;最后,提取训练完的模糊神经网络参数,以建立WSN节点故障诊断模型。实验结果表明,提出的无线数据收发单元故障诊断方法的计算量小,诊断准确度高;与高斯过程回归模型相比,其计算量降低了22.4%,诊断准确度提高了17.5%。
英文摘要:
      In some wireless sensor network(WSN) security monitoring systems,the nodes transfer large amounts of data in a long time,which causes the phenomenon of power decreasing and power amplifier(PA) being burned in the wireless data transceiver unit,but this kind of fault diagnosis method is generally complex and inefficient.In order to solve these problems,based on the analysis of WSN cell-level fault diagnosis,this paper proposed a fault diagnosis method based on fuzzy neural network by using the current model of wireless data transceiver unit.Firstly,according to the relationship between the emission current,the temperature and the supply voltage,the current model is established.Then,the fuzzy neural network model structure is determined by the clustering algorithm,and the hybrid learning algorithm is used to optimize the front and rear parameters of fuzzy rules.Finally,the fuzzy neural network parameters are extracted to establish the WSN node fault diagnosis model.The experimental results show that the presented fault diagnosis method of wireless data transceiver unit possesses low computational complexity and high diagnostic accuracy.Compared with Gaussian process regression model,the computational complexity of this method is reduced by 22.4%,and the diagnostic accuracy is increased by 17.5%.
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