# Clutch Pressure Plate Temperature Prediction Based on Bi-LSTM and Migration Learning
## 2 Clutch Temperature Prediction Model Construction Based on Bi-LSTM and Migration Learning
### Comparison  
The fitting results of three neural networks, BP, LSTM, and Bi-LSTM, on the validation set, are compared. The performance evaluation metrics such as _MSE_, _RMSE_, _MAE_, \(R^{2}\), maximum error, and maximum relative error are used for evaluation, as shown in Table 1.  
According to Table 1, for the Bi-LSTM model, its maximum error is below 10 \({}^{\circ}\)C, and the _MSE_, _RMSE_, and _MAE_ metrics are more convergent to 0, \(R^{2}\) is convergent to 1, the maximum error is 9.3 \({}^{\circ}\)C, and the maximum relative error is 3.15%. The metrics accuracy of the Bi-LSTM model is significantly higher than that of the BP model and the LSTM model.  
The temperature fitting results of the BP, LSTM, and Bi-LSTM models at three different locations on the platen are shown in Table 2, with a, b, and c being three different locations on the platen. The fitting results on the validation set are shown in Figure 6.  
According to Table 2, in the Bi-LSTM model metrics, the maximum error at position a is the largest, 9.13 \({}^{\circ}\)C, with a maximum relative error of 3.15%, and the maximum error at position c is the smallest, 4.72 \({}^{\circ}\)C, with a maximum relative error of 1.46%. In the LSTM model metrics, the maximum error value at position b is 56.42 \({}^{\circ}\)C, but the maximum relative error is the largest at position c, 18.27%. According to the images shown, the Bi-LSTM model fits closer to the actual values in the three positions.  
Although these three neural network methods can automatically learn and extract key features in the input data without requiring manual engineering features, this makes them suitable for various data types. However, BP neural network is a feedforward neural network, which usually does not have time linkage. On the contrary, LSTM and Bi-LSTM are recurrent neural network variants suitable for sequence data, with internal state and time linkage. LSTM can only transmit and process sequence data in one direction, while Bi-LSTM can transmit and process data in two directions. The output of each time step contains the context information before and after the time step, which makes the model prediction accuracy higher. Therefore, the Bi-LSTM model outperforms the other two algorithmic models in terms of clutch pressure plate temperature.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Indexers** & **a** & **b** & **c** \\ \hline \multirow{8}{*}{BP} & _MSE_ & 6.0406 & 9.2529 & 0.5062 \\  & _RMSE_ & 2.4578 & 3.0419 & 0.7115 \\  & _MAE_ & 2.3733 & 2.9833 & 0.5342 \\  & _R_\({}^{2}\) & 0.8149 & 0.7895 & 0.9884 \\  & Maximum error & 22.9318 & 34.3992 & 16.8411 \\  & Maximum relative error & 8.33\% & 10.49\% & 5.26\% \\ \hline \multirow{8}{*}{LSTM} & _MSE_ & 17.1884 & 13.4795 & 2.3014 \\  & _RMSE_ & 4.1459 & 3.6714 & 1.5170 \\  & _MAE_ & 4.0709 & 3.5449 & 1.2637 \\  & _R_\({}^{2}\) & 0.5551 & 0.7248 & 0.9647 \\  & Maximum error & 49.1469 & 56.4184 & 55.3713 \\  & Maximum relative error & 16.70\% & 17.45\% & 18.27\% \\ \hline \multirow{8}{*}{Bi-LSTM} & _MSE_ & 35.1228 & 5.7196 & 7.3462 \\  & _RMSE_ & 5.9265 & 2.3915 & 2.7103 \\ \cline{1-1}  & _MAE_ & 4.1935 & 2.1026 & 2.3962 \\ \cline{1-1}  & _R_\({}^{2}\) & 0.1939 & 0.9273 & 0.9414 \\ \cline{1-1}  & Maximum error & 9.1276 & 8.5856 & 4.7170 \\ \cline{1-1}  & Maximum relative error & 3.15\% & 2.66\% & 1.46\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Indicators for each model at each position (\({}^{\circ}\)C).  
In this paper, we will introduce migration learning and introduce part of the temperature data of the new platen for training to realize the high-accuracy prediction of the temperature of the new platen.