Because of the strong non-linearity and time-varying properties

and the existence of local minima

overlearning of conventional neural networks

a novel aero-engine identification model is introduced.The model is based on modern statistical learning theory

and Structure Risk Minimization (SRM) principle.It has very good generalization ability.By solving a quadratic convex programming problem

a global optimum can be automatically found.The identification model was established based on support vector regression machines using the real flight recorded data as learning samples.The results show that this method reveals high precision

good robustness and fault-tolerant ability. It provides a general way for aero-engine model identification.