# Fast Performance Evaluation for Switched-Capacitor Converter Based on Convolutional Neural Networks
## 5 Result and Analysis  
Compared to hours of parallel simulation, the CNN algorithm can greatly shorten the calculation time and only needs several minutes to obtain the evaluation result of six thousand topologies. The convergence curve of the CNN model is shown in Figure 9. After 30 iterations, the error function gradually became constant. A comparative experiment with alternative artificial neural network (ANN) and linear regression approaches was designed to verify the outstanding performance of the CNN model. The details on the artificial neural network model are shown in Table 4. The hyperparameters of the ANN were coordinated with those of the CNN, while the linear regression model can be expressed by  
\[\hat{y}=\mathbf{W}\cdot x+\mathbf{b} \tag{11}\]  
The prediction result of the different methods are shown in Figure 10. The red line represents the real efficiency data, while the blue line shows the prediction results. All the methods were able to predict the results to an extent, which proves the relationship between the topology and conversion efficiency. Prediction accuracy is the most intuitive evaluation indicator of model performance. Compared with linear regression and ANN, CNN is superior in prediction accuracy, as expected. The convolution algorithm can extract topology feature effectively and predict the operation performance based on its conversion structure. Table 5 shows the prediction error of each method. The MAPE of the CNN model is 0.013, while for linear regression it is 0.07 and for ANN it is 0.03. Although the convergence speed of CNN is slower than the other two methods, it nonetheless saves a great deal of time compared with parallel simulation and manual evaluation, which can accelerate the design process compared to the current state-of-the-art.  
\begin{table}
\begin{tabular}{c c} \hline
**Layer** & **ANN Model Parameter** \\ \hline Input layer & 25 neurons \\ Fully connected layer 1 & 128 neurons (ReLU) \\ Dropout layer & 0.5 \\ Fully connected layer 2 & Eight neurons (ReLU) \\ Dropout layer & 0.5 \\ Fully connected layer 3 & One neurons (Sigmoid) \\ \hline \end{tabular}
\end{table}
Table 4: Details of the ANN model.  
\begin{table}
\begin{tabular}{c c} \hline
**Layer** & **CNN Model Parameter** \\ \hline Input layer & 25 neurons \\ Conventional layer & Eight 3 \(\times\) 3 conv2d padding = 1 \\ Fully connected layer 1 & 128 neurons \\ Dropout layer & 0.5 \\ Fully connected layer 2 & Eight neurons \\ Dropout layer & 0.5 \\ Fully connected layer 3 & One neuron \\ \hline \end{tabular}
\end{table}
Table 3: Details of the CNN model.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **MSE** & **MAPE** & **Calculation Time (s)** \\ \hline CNN & 0.0072 & 0.013 & 403 \\ ANN & 0.0168 & 0.032 & 139 \\ Linear Regression & 0.0323 & 0.070 & 72 \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of implemented CNN model, ANN, and linear regression.  
These results show that the CNN model clearly outperforms the other two models. CNN can better fit the efficiency trend of different topologies, which clearly indicates it as a preferred choice for performance prediction of SCCs.