Research on sensors fault prognosis learning algorithm based on difference evolution cross validation SVM in the flight control system

The sensors fault prognosis scheme of the flight control system was established through Support Vector Regression (SVR) in the solution follows from the fault detection scheme of the modern fighter plane. This sensors fault diagnosis scheme which consisted of the modules of the actuator, the steering face is discussed to overcome the problem of fault trend predication. It shows how to obtain weighted estimates for regression by applying SVM/SVR. A method through the differential evolution to improve the original cross validation is presented. Moreover, it search optimization of kernel parameters for decrease the model errors, and improve the ability of generalization of the SVR model. And, it simulates the online faults in flight control system that proved feasibility of this prognosis algorithm.