Fault Diagnosis Research in Nonlinear Circuit Based on Improved Particle Swarm Optimization Algorithm

The feature extraction is key step to fault diagnosis in nonlinear circuit. An improved particle swarm optimization (PSO)algorithm are presented to competent for the identification and feature extraction problem in nonlinear circuit here. Firstly, the output response from the circuit under diagnosis will be collected and processing, some order frequency-domain kernel function under can be extracted as circuit feature for fault diagnosis. Secondly, the single feature or whole feature group of circuit can be seek by particle swarm optimization solution, the improved article swarm optimization algorithm apply to the searching for optimization solution of system identification based on frequency domain kernel of the nonlinear circuit. At last, the circuit diagnosis result will be give. The results shown: the algorithm is competent for the system identification optimization and pattern classification problem well, the diagnostic efficiency is high.