Combined Prediction of Aero-Engine Performance Index Based on Singular Value Trend Decomposition and LS-SVR
LI Don · Journal of Propulsion Technology · 2013
Engine performance index is one of most important index which weights good or bad performance. Aiming at performance index having characteristic of nonlinearity and nonstationary,multi- level and multi- scaling were introduced and a combined predicting method based on SVD( Singular Value Decomposition) was presented. Trend and fluctuation were extracted using SVD. The best parameter combination of LS- SVR( Least Square- Support Vector Regression) model in trend and fluctuation( embed dimension,delay time,punish coefficient,kernel parameter) was generated using improved PSO( Particle Swarm Optimization). The regression and motion were introduced. They were predicted respectively using best LS-SVR on this basis. The results indicate that predicting precision increases apparently and the computational time decreases. Precision changes unobviously when predicting step is within 5. The precision decreases rapidly when step is beyond 10. Compared with different methods,it verifies effectiveness of this method.