In research on intelligent shift for automatic transmission, the neural network selected has no feedback and lacks an associative memory function. Thus, its adaptability needs to be improved. To achieve this, an automatic shift strategy based on a deep recurrent neural network (DRNN) is proposed. First, a neural network framework was designed in combination with an eight-speed gearbox that matches a particular type of vehicle. Then, the working principle of the DRNN was applied to the shifting process of an automatic gearbox, and the implementation model of the shift logic was established in MATLAB/Stateflow. A data sample obtained from the model was used to train the DRNN. Training and evaluation of the DRNN were accomplished in Python. Finally, a simulation comparison of the DRNN with a back-propagation (BP) neural network proved that after the epochs have been increased, the DRNN has higher precision and adaptation than a BP neural network. This research provides a theoretical basis and technical support for intelligent control of automatic transmission.

在用于自动变速器的智能换档研究中，所选择的神经网络没有反馈并且缺乏联想记忆功能。因此，需要提高其适应性。为此，提出了一种基于深度递归神经网络（DRNN）的自动换挡策略。首先，将神经网络框架与匹配特定类型车辆的八速变速箱结合在一起进行设计。然后，将DRNN的工作原理应用于自动变速箱的换挡过程，并在MATLAB / Stateflow中建立了换挡逻辑的实现模型。从模型获得的数据样本用于训练DRNN。DRNN的培训和评估是使用Python完成的。最后，DRNN与反向传播（BP）神经网络的仿真比较证明，在增加历元之后，DRNN具有比BP神经网络更高的精度和适应性。该研究为自动变速器的智能控制提供了理论基础和技术支持。