# Prediction of Ship-Unloading Time Using Neural Networks
## 3 Related Methods
### Stochastic Configuration Networks
Figure 4: Fitting curves on DB1. Figure 5: Fitting curves on DB2. Figure 3: Fitting curves on DB4. Figure 2: Fitting curves on DB3. ### Stability

Stability is an important measure of an algorithm's performance. For the same reason, the RVFL is not inspected; only the BP and the SCN are discussed for model stability. Ibid., four data sets are used for testing, and 100 trials are executed. The RMSE is used as the evaluation measure. The computational results are shown in Figures 10-13. Compared with the BP, the RMSE curves of the SCN have smaller values and fluctuations, which indicates that the SCN has better stability and prediction accuracy with the algorithm proceeding. Figure 11: RMSE within 100 trials (DB2). Figure 10: RMSE within 100 trials (DB1). Figure 9: Loss curve of training (DB4). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Range of Weights**} & \multicolumn{3}{c}{**DB3**} \\ \cline{2-5}  & **RVFL** & **BP** & **SCN** \\ \hline \([-1,1]\) & 0.07325 & 0.04593 & 0.03709 \\ \([-5,5]\) & 0.04759 & 0.50602 & 0.07701 \\ \([-10,10]\) & 0.04287 & 0.57113 & 0.07612 \\ \([-20,20]\) & 0.07698 & 0.04606 & 0.07848 \\ \([-30,30]\) & 0.09410 & 0.55866 & 0.10125 \\ \([-40,40]\) & 0.10406 & 0.52318 & 0.09185 \\ \([-50,50]\) & 0.12092 & 0.53317 & 0.08473 \\ \([-60,60]\) & 0.12194 & 0.53291 & 0.08901 \\ \([-80,80]\) & 0.13108 & 0.53027 & 0.08757 \\ \([-100,100]\) & 0.12379 & 0.54040 & 0.08571 \\ \([-500,500]\) & 0.13252 & 0.55592 & 0.08917 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance with various weights on DB3. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Range of Weights**} & \multicolumn{3}{c}{**DB4**} \\ \cline{2-5}  & **RVFL** & **BP** & **SCN** \\ \hline \([-1,1]\) & 0.18387 & 0.09637 & 0.10241 \\ \([-5,5]\) & 0.13977 & 0.40924 & 0.10093 \\ \([-10,10]\) & 0.14436 & 0.57055 & 0.09312 \\ \([-20,20]\) & 0.17075 & 0.55035 & 0.10734 \\ \([-30,30]\) & 0.19714 & 0.56227 & 0.13241 \\ \([-40,40]\) & 0.20660 & 0.53162 & 0.12498 \\ \([-50,50]\) & 0.21126 & 0.55677 & 0.10198 \\ \([-60,60]\) & 0.21676 & 0.55245 & 0.10081 \\ \([-80,80]\) & 0.22001 & 0.5657 & 0.10748 \\ \([-100,100]\) & 0.23056 & 0.55152 & 0.09669 \\ \([-500,500]\) & 0.24482 & 0.50343 & 0.10091 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance with various weights on DB4. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**DB1**} & \multicolumn{3}{c}{**DB2**} & \multicolumn{3}{c}{**DB3**} \\ \cline{2-5}  & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** \\ \hline RVFL & 0.13695 & 0.08663 & 0.14349 & 0.06779 & 0.09719 & 0.03276 & 0.19690 & 0.03396 \\ BP & 0.54425 & 0.13744 & 0.52384 & 0.10962 & 0.44942 & 0.20026 & 0.49548 & 0.14010 \\ SCN & 0.00873 & 0.00160 & 0.00863 & 0.00213 & 0.08164 & 0.01646 & 0.10628 & 0.01192 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Algorithm stability with various initial weights. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Range of Weights**} & \multicolumn{3}{c}{**DB2**} \\ \cline{2-5}  & **RVFL** & **BP** & **SCN** \\ \hline \([-1,1]\) & 0.18356 & 0.20413 & 0.00474 \\ \([-5,5]\) & 0.12167 & 0.52918 & 0.00704 \\ \([-10,10]\) & 0.09808 & 0.54208 & 0.00817 \\ \([-20,20]\) & 0.09679 & 0.53724 & 0.00582 \\ \([-30,30]\) & 0.08942 & 0.57866 & 0.00853 \\ \([-40,40]\) & 0.09056 & 0.60472 & 0.00962 \\ \([-50,50]\) & 0.10127 & 0.56014 & 0.00937 \\ \([-60,60]\) & 0.14049 & 0.59212 & 0.00942 \\ \([-80,80]\) & 0.16943 & 0.56078 & 0.00966 \\ \([-100,100]\) & 0.16799 & 0.51073 & 0.01021 \\ \([-500,500]\) & 0.31912 & 0.54241 & 0.01235 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance with various weights on DB2. ### Case Study

A real-world application of the unloading-time prediction will be addressed using the three models in this section. The data originates from the raw materials terminal of the studied steel plant, covering the years 2018 to 2020.