SVM fault diagnosis of autopilot based on quantum inspired gravitational search algorithm

With regard to the lack of the sample of faults in the test of autopilot, a model of fault diagnosis based on support vector machine (SVM) optimized by quantum inspired gravitational search algorithm is put forward. SVM does well in solving the few samples and nonlinear problem, which is suitable for the fault diagnosis of autopilot. To improve the convergence rate and accuracy of parameters optimizing based on gravitational search algorithm (GSA), quantum inspired gravitational search algorithm (QGSA) was applied to optimizing the parameters of SVM. SVM based on QGSA can solve the overfitting and underfitting resulted from the improper parameters. By this way, a model of fault diagnosis with better performance was built. The simulation experiment results show that the accuracy of SVM based on QGSA can achieve 96.530 6% using 50 training samples. However, the accuracy of genetic algorithm (GA)-SVM achieves 92.040 8% and the accuracy of SVM based on GSA achieves 91.632 7%. The simulation experiment results shows that SVM based on QGSA has much better performance than others.