The characters and difficulties in the test for electronic-controlled gasoline engine of unmanned aerial vehicle (UAV) were analyzed in detail.For mending the traditional liner interpolation methods

which are not completely in conformity with nonlinear characteristics of electronic-controlled gasoline engine

a solution was applied in the predication for the manifold air pressure (MAP) of high-altitude calibration of electronic-controlled gasoline engine

by adopting back propagation (BP) neural networks.To avoid the limitation of neural networks in application at present stage

a grouping method of raw-data was put forward to control the feedback of the training error in real-time;this method provided a good solution for the problem that the neural network training result easily leads to the situation of "local optimum" and "over-fitting".The prediction results based on BP neural networks have been studied thoroughly;and the results of the training error and the prediction error reveal that the method is feasible and the result is effective.