Analog Circuit Fault Diagnosis Based on Principal Component Analysis of Pretreatment and Particle Swarm Hybrid Neural Network

Abstract

With the rapid development of electronic technology, the system reliability and economic requirements of the importance of the analog circuit fault diagnosis has become increasingly prominent. Aiming at the shortcomings of traditional diagnosis method, the paper presents an analog circuit fault diagnosis method based on principal component analysis of pretreatment and particle swarm hybrid neural network. The method adopts hybrid algorithm to adjust the network weights and thresholds to avoid falling into the local minimum value, which uses principal component pretreatment effectively reduce the complexity of calculation. Simulation results show that the diagnostic method can be used for tolerance analog circuit fault diagnosis, has better application prospect.