## 3 Experimental Results
### Experimental Results Obtained with the FT-CFNN  
As presented in Table 4, the accuracy obtained with CGAP fusion was higher than that achieved with the other three fusion methods. Because the network architecture parameters \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Fusion Methods** & **Accuracy** \\ \hline \multirow{4}{*}{CFNN} & GMP & 97.86\% \\ \cline{2-3}  & GAP & 96.68\% \\ \cline{2-3}  & CGMP & 97.64\% \\ \cline{2-3}  & CGAP & 98.13\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy of the CFNN with various fusion methods. \begin{table}
\begin{tabular}{c c c} \hline \hline Image Size & \(224\times 224\times 3\) \\ \hline Epochs & 50 \\ \hline Learning rate & 0.001 \\ \hline Batch size & 64 \\ \hline Image Size & \(224\times 224\times 3\) \\ \hline Epochs & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Learning parameter settings of the CFNN. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Kernel Size** & **Number** & **Layer** & **Kernel Size** \\ \hline Convolution\_1 & \(3\times 3\) & 32 & 2 & 0 \\ \hline Max\_pooling\_1 & \(2\times 2\) & - & 2 & \\ \hline Convolution\_2 & \(3\times 3\) & 64 & 2 & 0 \\ \hline Max\_pooling\_2 & \(2\times 2\) & - & 2 & \\ \hline Feature fusion & - & 64 & - & \\ \hline Fuzzy Rule Layer & - & 64 & - & \\ \hline DeFuzzify Layer & - & 25 & - & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Network architecture parameter settings of the CFNN. could not be easily determined, the Taguchi method was used to determine the impact factor and optimize the parameter combinations. Therefore, the developed FT-CFNN uses the CGAP fusion method to apply the Taguchi method. In the conducted experiment, six factors were selected: Conv1-Filter (F1), Conv1-Kernel size (K1), Conv1-Padding (P1), Conv2-Filter (F2), Conv2-Kernel size (K2), and Conv2-Padding (\(P2\)). Four of these factors (F1, K1, F2, and K2) are three-level factors, and the other two factors (P1 and P2) are two-level factors. The affecting factors and their levels are presented in Table 5. According to the total number of factors and levels in the experiment, we used the L36 orthogonal table, which is composed of different factors and levels. The selected three- and two-level factors required \(2^{2}\times 3^{4}\) = 324 traditional experiments. After the Taguchi method was applied, only 36 experiments were required. Therefore, the Taguchi method reduced the number of experiments required. To ensure the stability of the experiments, three experiments were performed for each parameter combination in the OA, and the average of the three accuracy values was used to calculate the S/N ratio, as presented in Table 6. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**F1**} & \multirow{2}{*}{**K1**} & \multirow{2}{*}{**P1**} & \multirow{2}{*}{**F2**} & \multirow{2}{*}{**K2**} & \multirow{2}{*}{**P2**} & \multicolumn{2}{c}{**Expt. 1**} & \multirow{2}{*}{**Expt. 2**} & \multirow{2}{*}{**Expt. 3**} & \multirow{2}{*}{**Average**} & \multicolumn{1}{c}{\multirow{2}{*}{**S/N**}} \\  & & & & & & & & & & & & \\  & & & & & & & & & & & & \\ \hline  
\hline \end{tabular}
\end{table} 
Table 6: Accuracy values and S/N ratios for different parameter combinations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**No.**} & \multicolumn{1}{c}{**Abbreviation**} & \multicolumn{1}{c}{**Affecting Factors**} & \multicolumn{1}{c}{**Level 1**} & \multicolumn{1}{c}{**Level 2**} & \multicolumn{1}{c}{**Level 3**} \\ \hline A & F1 & Conv1-Filter & 8 & 16 & 32 \\ \hline B & K1 & Conv1-Kernel size & \(3\times 3\) & \(5\times 5\) & \(6\times 6\) \\ \hline C & P1 & Conv1-Padding & 0 & 1 & \\ \hline D & F2 & Conv2-Filter & 16 & 32 & 64 \\ \hline E & K2 & Conv2-Kernel size & \(3\times 3\) & \(5\times 5\) & \(6\times 6\) \\ \hline F & P2 & Conv2-Padding & 0 & 1 & \\ \hline \end{tabular}
\end{table} 
Table 5: Affecting factors and their levels.