Fault Diagnosis Model Based on the Fusion of Hybrid Neural Network And Ant Colony Optimization Algorithm

Abstract

In this paper, fault diagnosis model based on the fusion of hybrid neural network (HNN) and ant colony optimization algorithm (ACOA) is presented. The associated rules are extracted based on rough set theory and are used as the theoretic basis of the connection mechanism of higher-order NN, which was composed with the feedforwardNN, the hybrid NN model is constructed. ACOA was used to optimize solving and to improve the generalization performance of HNN model. Hence the construction of presented model possesses theoretical significance and may ensure to take optimization performance and to enhance the generalization ability. Through the simulation and the analysis of fault-tolerance performance (FTP), it shows that the model based on the fusion of HNN and ACOA can effectively enhance the generalization ability and the FTP.