A new modeling method for engine dynamic characteristics based on assembled neural networks

Focusing on the defects of current assembled artificial neural network (ANN) models, its weak generalization ability for engine experiment sample data of different array structure, multi-step linear interpolation method (MLIM for short), a new assembled ANN modeling method, was put forward, which was based on finite element method. In MLIM, using one-dimensional input vector with abundant sample data, some mesh lines were set up to make a division of the input space. The sample data on these mesh lines was brought in BP neural model training process, from which some high-precision artificial neural network functions were obtained. Output of sample data between meshing lines was multi-step linearly interpolated by the most two neighboring mesh line ANN function value. Compared with traditional assembled neural network modeling methods, MLIM has good adaptability in processing multi-dimensional engine dynamic characteristic testing data with different input array length.