Research of Gear Position Decision of Dual Motor Multiple Speed AMT based on BP and Its Optimization Neural Network - Yongqiang Ren; Qisheng Wu; Biao Yuan (Jixie chuandong)
By using the three-parameter shift law， the algorithm and parameter optimization are carried out to overcome the shortcomings of traditional BP neural network in optimized BP neural network of gear position decision， and then the optimized BP neural network is obtained. Taking the dual motor multiple speeds AMT of pure electric vehicle as the object， the experimental data are collected， the neural network before and after optimization is constructed， and then the training and simulation analysis are carried out. It can be concluded that the neural network has a faster learning speed by training the neural network. By analyzing the training process， it is concluded that the optimized neural network has faster learning speed， and the optimized neural network gear position decision model has higher accuracy through simulation analysis of the corresponding model after training， and the optimized parameters provide reference value for the corresponding theoretical research.