Computer Science ›› 2016, Vol. 43 ›› Issue (Z11): 21-25.doi: 10.11896/j.issn.1002-137X.2016.11A.005

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WSN Fault Diagnosis with Improved Rough Set and Neural Network

ZHOU Xi and XUE Shan-liang   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Integrating the advantages of rough set theory and artificial neural network,improved rough set theory algorithm was proposed,and combined with artificial neural network,an intelligent fault diagnosis method of wireless sensor network (WSN) node was achieved.First,based on the analysis of application environment of WSN and fault characte-ristics,diagnosis decision table is obtained through data acquisition,data pretreatment and data compression,and is reduced by the improved inductive attribute reduction algorithm (IIARA) of rough set,thus the minimum fault diagnosis feature set is extracted which contributed most to the fault diagnosis,and then the topology of the radial basis function neural network (RBFNN) is determined.Finally,diagnosis results are obtained through the nonlinear mapping relationship between fault symptoms and fault types established by the network training.Simulation results show that at the time of fault diagnosis of WSN nodes,the diagnosis algorithm can effectively reduce network input layer,simplify the structure of neural network,reduce the training time of the network and improve the diagnosis accuracy of the models.

Key words: Fault diagnosis,Rough set,Inductive attribute reduction algorithm,Radial basis function,Artificial neural network,Wireless sensor network

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