# A Convex Combination Approach for Artificial Neural Network of Interval Data
## 3 The Proposed Method Artificial Neural Network with Convex Combination (ANN-CC)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**One Hidden Neuron** & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**MSE**} & \multicolumn{2}{c}{**RMSE**} \\ \hline PTT & out & in & out & in & out & in \\ \hline tanh & 0.020572 & 0.020551 & 0.000699 & 0.000633 & 0.026443 & 0.025164 \\ sigmoid & 0.019717 & 0.019512 & 0.000641 & 0.000612 & 0.025322 & 0.024745 \\ linear & 0.011574 & 0.014203 & 0.000348 & 0.000239 & 0.018662 & 0.015467 \\ exp & **0.011498** & **0.014183** & **0.000347** & **0.000204** & **0.018634** & **0.014291** \\ \hline \hline  & MAE & \multicolumn{2}{c}{MSE} & \multicolumn{2}{c}{RMSE} \\ \hline SCC & out & in & out & in & out & In \\ \hline tanh & **0.017615** & **0.017566** & **0.000461** & **0.000463** & **0.021466** & **0.021522** \\ sigmoid & 0.017725 & 0.017619 & 0.000529 & 0.000470 & 0.023012 & 0.021684 \\ linear & 0.018800 & 0.017891 & 0.000541 & 0.000487 & 0.023267 & 0.022070 \\ exp & 0.018008 & 0.017821 & 0.000519 & 0.000480 & 0.022791 & 0.021915 \\ \hline \hline  & MAE & \multicolumn{2}{c}{MSE} & \multicolumn{2}{c}{RMSE} \\ \hline CPALL & out & in & out & in & out & in \\ \hline tanh & 0.020698 & 0.020667 & 0.000679 & 0.000673 & 0.026053 & 0.025945 \\ sigmoid & 0.020784 & 0.020711 & 0.000735 & 0.000675 & 0.027123 & 0.025978 \\ linear & 0.020946 & 0.020754 & 0.000785 & 0.000730 & 0.028028 & 0.027032 \\ exp & **0.018078** & **0.016966** & **0.000571** & **0.000516** & **0.023883** & **0.022729** \\ \hline \hline
**Two Hidden Neurons** & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**MSE**} & \multicolumn{2}{c}{**RMSE**} \\ \hline PTT & out & in & out & in & out & in \\ \hline tanh & 0.020031 & 0.019463 & 0.000657 & 0.000611 & 0.025640 & 0.024736 \\ sigmoid & 0.009529 & 0.009196 & 0.000186 & 0.000165 & 0.013632 & 0.012854 \\ linear & 0.019771 & 0.019383 & 0.000608 & 0.000610 & 0.024667 & 0.024689 \\ exp & **0.009449** & **0.009187** & **0.000184** & **0.000160** & **0.013573** & **0.012657** \\ \hline \hline  & MAE & \multicolumn{2}{c}{MSE} & \multicolumn{2}{c}{RMSE} \\ \hline SCC & out & in & out & In & out & In \\ \hline tanh & 0.018178 & 0.017976 & 0.000517 & 0.000506 & 0.022743 & 0.022488 \\ sigmoid & 0.013537 & 0.013251 & 0.000314 & 0.000294 & 0.017732 & 0.017155 \\ linear & 0.013337 & 0.013334 & 0.000295 & 0.000300 & 0.017182 & 0.017329 \\ exp & **0.013058** & **0.013048** & **0.000285** & **0.000281** & **0.016898** & **0.016773** \\ \hline \hline  & MAE & \multicolumn{2}{c}{MSE} & \multicolumn{2}{c}{RMSE} \\ \hline
**C**PLL & out & in & out & in & out & in \\ \hline tanh & **0.014284** & **0.014469** & **0.000359** & **0.000406** & **0.018953** & **0.020149** \\ sigmoid & 0.014353 & 0.014793 & 0.000393 & 0.000415 & 0.019832 & 0.020377 \\ linear & 0.020375 & 0.020873 & 0.000663 & 0.000698 & 0.025759 & 0.026432 \\ exp & 0.020621 & 0.020821 & 0.000675 & 0.000696 & 0.025989 & 0.026378 \\ \hline \hline  & MAE & \multicolumn{2}{c}{MSE} & \multicolumn{2}{c}{RMSE} \\ \hline tanh & **0.014284** & **0.014469** & **0.000359** & **0.000406** & **0.018953** & **0.020149** \\ sigmoid & 0.014353 & 0.014793 & 0.000393 & 0.000415 & 0.019832 & 0.020377 \\ linear & 0.020375 & 0.020873 & 0.000663 & 0.000698 & 0.025759 & 0.026432 \\ exp & 0.020621 & 0.020821 & 0.000675 & 0.000696 & 0.025989 & 0.026378 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results of regularized ANN-LU (RANN-LU).