Abstract
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.
Highlights
The proportion of automatic transmission (AT) vehicles in the market has been increasing significantly because they are easy to operate, make drivers comfortable, and reduce driver fatigue.[1]
We find that deep recurrent neural network (DRNN) works best when the number of hidden layer nodes is 13
When the data is inputted to both NNs, the weights in both NNs will be modified based on the algorithm introduced earlier, which makes both neural networks achieve the expected level of precision
Summary
The proportion of automatic transmission (AT) vehicles in the market has been increasing significantly because they are easy to operate, make drivers comfortable, and reduce driver fatigue.[1]. The BPNN gear shifting prediction model is set as the control group, and the simulation result shows that DRNN is superior to BPNN in prediction accuracy. By comparing the actual output with the theoretical output and calculating their difference, we obtain the loss function and error term, which are used to modify the weight matrix between layers in the
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