Modeling for landing process of a helicopter with rotator self-rotating based on support vector machine

As a system identification method, Neural Network can be applied to build the simulation model of a helicopter. But it has some difficulties such as the hardness of selecting network structure, slow convergence speed, local minimum, and generalization ability question. To avoid the question above, the Support Vector Machine (SVM) method is introduced to the field of flight simulation for the first time, and the rotator speed model for landing process of a helicopter with rotator self-rotating is built. Compared with the Neural Network model, the SVM simulation model of a helicopter owns some advantages such as simple structure, fast convergence speed and high generalization ability. It is shown by theoretic analysis and simulation result that the SVM method is feasible.