Fault prediction for aircraft control surface damage based on SMO-SVR

In order to predict changes more accurately when the surface of aircraft damaged, an algorithm based on improved sequential minimal optimization support vector regression (SMO-SVR) was presented. This algorithm reconstructed the phase space of multivariate and nonlinear time series using improved C-C average method to determine the embedding dimension m and the delay time τ_d. Then, a weighted SVR model was built according to m and τ_d, and in which the halt criterion of SMO was modified. The parameters of SVR were optimized by interval adaptive particle swarm optimization (IAPSO) to improve the efficiency of parameter optimization. In order to verify the validity of the algorithm, the prediction and analysis of surface damage trend were performed. Comparing with the radial basis function neural network (RBFNN) method, the simulation result demonstrates that the improved SMO-SVR prediction model has good predictive ability.