# Conveyor Belt Damage Detection with the Use of a Two-Layer Neural Network
## 3 Result and Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Neurons** & **Correctly Detected** & **Accuracy [\%]** \\
**in the Hidden Layer** & **Correctly Detected** & **Accuracy [\%]** \\ \hline \(k=1\) & 69 & 8.63 \\ \(k=3\) & 218 & 27.25 \\ \(k=8\) & 616 & 77.00 \\ \(k=20\) & 656 & 82.00 \\ \(k=100\) & 670 & 83.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Neural network accuracy. Figure 9: Number of epochs until the end of the learning process. For the best selected number of neurons in the hidden layer (\(k=20\)), tests were carried out on the impact of the selected activation function in the hidden layer of the neural network on the quality of recognition. For this purpose, four functions were used: _logsig_, _radbas_, _softmax_, _tausig_, and the test results were collected in Table 4.  
As before, in the learning process of the neural network, the MSE indicator was observed, and the learning process was interrupted when the MSE value on the validation set began to increase (the patience parameter was set to 6). Table 5 contains averaged parameters obtained in the network training process. A classification is considered correct if one of the network outputs assumes a value above the predefined acceptance threshold, and all of the remaining outputs assume values below the rejection threshold, with the values being 0.6 for the acceptance threshold and 0.4 for the rejection threshold. Table 6 collects the network performance data with the activation function selected. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Activation** & \multicolumn{8}{c}{**Network Output**} \\
**Function** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline \multirow{9}{*}{_logsig_} & 0.91 & 0.00 & \(-\)0.01 & 0.02 & 0.01 & \(-\)0.04 & 0.07 & 0.05 & 0.00 \\  & 0.00 & 0.00 & 1.02 & 0.01 & 0.04 & \(-\)0.03 & \(-\)0.10 & 0.07 & 0.00 \\  & 0.01 & 0.00 & 0.00 & 0.71 & 0.22 & 0.07 & \(-\)0.01 & 0.00 & 0.00 \\  & 0.04 & 0.00 & 0.00 & \(-\)0.06 & 0.97 & 0.06 & \(-\)0.02 & 0.00 & 0.00 \\  & \(-\)0.06 & 0.00 & \(-\)0.01 & 0.04 & 0.07 & 0.88 & 0.01 & 0.05 & 0.00 \\  & 0.01 & 0.00 & 0.03 & \(-\)0.01 & \(-\)0.02 & 0.10 & 0.68 & 0.20 & 0.00 \\  & 0.04 & 0.00 & \(-\)0.03 & \(-\)0.02 & 0.04 & 0.04 & 0.23 & 0.65 & 0.00 \\  & \(-\)0.03 & 0.00 & \(-\)0.05 & 0.00 & 0.04 & \(-\)0.03 & 0.07 & 0.13 & 0.80 \\ \hline \multirow{9}{*}{_radbas_} & 0.98 & 0.00 & 0.00 & 0.01 & 0.00 & \(-\)0.03 & 0.01 & 0.06 & 0.00 \\  & 0.00 & 0.00 & 1.03 & 0.01 & 0.01 & 0.01 & \(-\)0.06 & 0.03 & 0.00 \\  & \(-\)0.01 & 0.00 & 0.01 & 0.74 & 0.21 & 0.05 & 0.00 & \(-\)0.01 & 0.00 \\  & 0.02 & 0.00 & 0.01 & \(-\)0.04 & 0.98 & 0.03 & \(-\)0.02 & 0.00 & 0.00 \\  & \(-\)0.02 & 0.00 & 0.00 & 0.01 & 0.00 & 0.98 & 0.00 & 0.01 & 0.00 \\  & \(-\)0.04 & 0.00 & 0.01 & \(-\)0.01 & \(-\)0.01 & 0.14 & 0.78 & 0.12 & 0.00 \\  & 0.00 & 0.00 & \(-\)0.02 & \(-\)0.01 & 0.02 & 0.04 & 0.23 & 0.68 & 0.00 \\  & 0.08 & 0.00 & 0.00 & 0.12 & 0.17 & \(-\)0.10 & \(-\)0.06 & 0.10 & 0.61 \\ \hline \multirow{9}{*}{_softmax_} & 0.91 & 0.00 & 0.01 & 0.02 & 0.00 & \(-\)0.04 & 0.10 & 0.00 & 0.00 \\  & 0.01 & 0.00 & 0.89 & 0.01 & 0.03 & 0.00 & 0.00 & 0.05 & \(-\)0.01 \\  & 0.03 & 0.00 & 0.01 & 0.54 & 0.22 & 0.21 & 0.02 & \(-\)0.03 & 0.00 \\  & 0.04 & 0.00 & 0.01 & \(-\)0.07 & 0.92 & 0.13 & \(-\)0.02 & \(-\)0.03 & 0.00 \\  & \(-\)0.08 & 0.00 & \(-\)0.02 & 0.22 & 0.20 & 0.47 & 0.03 & 0.17 & 0.00 \\  & \(-\)0.02 & 0.00 & 0.11 & \(-\)0.01 & \(-\)0.02 & 0.05 & 0.55 & 0.32 & 0.00 \\  & 0.07 & 0.00 & \(-\)0.05 & \(-\)0.02 & 0.02 & 0.08 & 0.33 & 0.54 & 0.00 \\  & \(-\)0.01 & 0.00 & \(-\)0.06 & 0.00 & 0.00 & 0.00 & 0.02 & 0.03 & 0.73 \\ \hline \multirow{9}{*}{_tansig_} & 0.95 & 0.00 & \(-\)0.01 & 0.02 & 0.00 & \(-\)0.02 & 0.05 & 0.04 & 0.00 \\  & 0.02 & 0.00 & 1.03 & 0.00 & 0.03 & 0.01 & \(-\)0.09 & 0.04 & 0.00 \\ \cline{1-1}  & 0.01 & 0.00 & 0.00 & 0.72 & 0.24 & 0.05 & \(-\)0.