Assessment on Performance of Fault Diagnosis Model Based on Fault-tolerance RBF-NN Using Ant Colony Algorithm
Shiying Zhang · 2007
In this paper,the fault diagnosis model is presented based on the fault-tolerance radial basis function neural network(RBF-NN)using ant colony optimization algorithm(ACOA).RBF-NN possesses excellent approaching ability,and its generalization ability can be further improved by ACOA.It is also considered in the paper that the basic fault pattern(BFP)can be formed into variational fault pattern(VFP)when disturbed by the stochastic factors,and the fault-tolerance performance(FTP)can be enhanced by training the NN with VFPs.The proposed model is used for fault diagnosis of power transmission and distribution systems,and the FTP is assessed in the paper.Simulation results show the the FTP of the proposed model is superior to that of the conventional diagnosis model based on BP-NN and GA-NN,which prove its feasibility.