摘要
提出了一种适用于无线传感器网络WSN的故障检测方法,该方法运用改进的递归神经网络MRNN为WSN的节点、节点的动态特性以及节点间的关系建立相关模型,对WSN节点进行识别和故障检测。MRNN的输入选择建模节点的先前输出值及其邻居节点的当前及先前输出值,模型基于一种新的改进的反向传播型神经网络,该神经网络的输入以及传感器网络的拓扑结构基于通用的非线性传感器模型。仿真实验将MRNN方法与卡尔曼滤波法进行了全面的比较。实验表明,MRNN在置信因子较小的情况下与卡尔曼滤波方法相比有较高的故障检测精度。
We present a novel sensor node fault detection method for wireless sensor network （WSN）. Modified Recurrent Neural Network （MRNN） is used to model sensor nodes, the nodes＇ dy- namics, and the interconnections with other sensor network nodes. An MRNN modeling approach is used for sensor node identification and fault detection in WSN. The input to the MRNN chooses those that include previous output samples of the modeling sensor nodes, and the current and previous output samples of the neighboring sensors. The model is based on a new structure of a back-propagation type neural network. The input to the MRNN and the topology of the network are based on a general nonlinear sensor model. Simulation results demonstrate the effectiveness of the proposed scheme and the MRNN method has higher failure detection accuracy in the case with smaller confidence factors compared with the Kalman filter method.
出处
《计算机工程与科学》
CSCD
北大核心
2015年第4期711-718,共8页
Computer Engineering & Science
基金
山东省高等学校科技计划项目(J13LN55)
济南市高校院所自主创新科技计划项目(201303017)
山东英才学院校级科研课题(12YCYBZR01)
关键词
故障检测
建模
递归神经网络
无线传感器网络
fault detection
modeling
recurrent neural networks
wireless sensor network
