# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 5 Numerical Experimental Analysis
### Comparative Analysis with Other Intention Recognition Methods  
In this experiment, we compared the proposed ResCNN model with CNN, LSTM, and GAN models, evaluating their classification performance on the same dataset. To ensure fairness, all models used identical training and testing sets, with consistent optimizers and early stopping mechanisms. Hyperparameters for all models were tuned using grid search to ensure optimal configurations for the experiments. We use the accuracy metric in classification problems to evaluate the performance of each method. The intent recognition results are shown in Figure 9 and Table 7. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Batch Size**} & \multicolumn{6}{c}{**Accuracy (\%)**} \\ \cline{2-7}  & \multicolumn{3}{c}{\(n_{e}\) = 350} & \multicolumn{3}{c}{\(n_{e}\) = 400} \\ \cline{2-7}  & \(lr\) = 0.01 & \(lr\) = 0.001 & \(lr\) = 0.0001 & \(lr\) = 0.01 & \(lr\) = 0.001 & \(lr\) = 0.0001 \\ \hline \(n_{b}=128\) & 94.42 & 94.98 & 90.89 & 94.81 & 95.53 & 93.83 \\ \(n_{b}=256\) & 94.68 & 95.40 & 94.68 & 95.10 & 95.83 & 94.93 \\ \(n_{b}=512\) & 95.02 & 95.57 & 94.38 & 95.10 & 95.57 & 95.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The accuracy of AUV intent recognition under different hyperparameters. The results demonstrate that the proposed ResCNN model outperforms the other models, achieving a classification accuracy of 95.83%. This improvement is primarily attributed to the introduction of residual connections, which effectively mitigate the vanishing gradient problem, allowing the network to maintain strong training performance even at deeper layers. The CNN model achieved an accuracy of 94.98%, ranking second among the compared models. While CNN effectively captures local features in the data, its performance is somewhat limited by the absence of a deeper network structure. The LSTM model achieved an accuracy of 94.55%, slightly lower than both CNN and ResCNN, suggesting that the advantages of LSTM in time-series modeling did not fully translate into better performance for the current mission, potentially due to its weaker ability to extract local spatial features. The GAN model achieved an accuracy of 94.85%, similar to CNN, but slightly lower than ResCNN, indicating that while GAN generates richer feature representations, its classifier and feature extraction capabilities did not fully match those of ResCNN. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Accuracy (\%)** \\ \hline ResCNN & 95.83 \\ CNN & 94.98 \\ LSTM & 94.55 \\ GAN & 94.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The intent recognition accuracy for different methods. Figure 9: The confusion matrix results for different methods. From the training process shown in Figure 10, it can be observed that the training loss curve of ResCNN shows an overall rapid decrease, and it converges to a relatively low loss value quickly within the first 50 epochs. As training progresses, the loss fluctuations gradually decrease and eventually stabilize, demonstrating good convergence. The training loss curve of CNN also declines quickly, but there are larger fluctuations in the later stages, especially after 250 epochs, where the loss does not show significant improvement and the fluctuation amplitude is higher than that of ResCNN. The training loss curve of LSTM exhibits a clear downward trend, with rapid loss reduction within the first 50 epochs, followed by a gradual stabilization, maintaining low fluctuations in the later stages. The training loss curve of GAN decreases slowly in the early stages, and its fluctuations are significantly higher than those of the other models throughout the entire training process. Even after a substantial number of epochs, the loss still shows noticeable oscillations. From the analysis of the loss curves, it is clear that ResCNN outperforms the other methods in terms of training, with faster convergence and higher stability.