Analogue Circuit Fault Diagnosis Based on SVM Optimized by IGSA

This paper proposes a modified classifier that uses the improved gravity search algorithm (IGSA) to optimize the parameter of SVM (IGSA-SVM) by introducing the inertia weight and global memory in particle swarm algorithm, time-varying gravitational search strategy and boundary mutation strategy.At first, three UCI datasets are selected for simulation analysis, and the results show that IGSA-SVM classifier is better than GS-SVM, GASVM, PSO-SVM and GSA-SVM classifiers in classification accuracy and classification time.Then the linear and nonlinear analog circuits are used as fault diagnosis circuits respectively, and the results show that the IGSA-SVM classifier can effectively prevent local convergence and improve the efficiency of diagnosis.