# A Novel UCP Model Based on Artificial Neural Networks and Orthogonal Arrays
## 3 New, Improved UCP--Our Approach
### The Methodology Used within the Improved UCP Model
#### Step 3:
A graphical representation of the GA values, during four iterations, is shown in Figure 5.  
A graphical representation of the MRE value for the Winner network, relative to the MMRE value on the training dataset, during the four iterations, is given in Figure 6.  
Table 6 shows the results obtained by training the second proposed ANN-36 prim architecture on the used dataset. The number of iterations concerning the set GA criterion was monitored. The GA criterion was met after six iterations. Based on all MRE values in each executed iteration, the \"Winner\" network was determined, i.e., the ANN network with the lowest MRE value. Additionally, the MMRE value was calculated for each iteration. The obtained value of the \"Winner\" network (ANN10) is 6.9%, and the value for MMRE is 7.0%. A graphical representation of GA values, during six iterations, is shown in Figure 7.  
\[\text{OA(ANN}i)=Yi,\text{ where }i=16,i=36. \tag{30}\]0 GA for ANN-L16-training part (UCP). \[\text{OA(ANN}i)=Yi,\text{ where }i=16,i=36. \tag{30}\]1 “Winner” MRE vs. MMRE on the training dataset (ANN-L16). \[\text{OA(ANN}i)=Yi,\text{ where }i=16,i=36. \tag{30}\]2 GA for ANN-L36 prim-training part (UCP). A graphical representation of the MRE value for the Winner network, relative to the MMRE value on the training dataset, during six iterations, is given in \[\text{OA(ANN}i)=Yi,\text{ where }i=16,i=36. \tag{30}\]3  
The obtained results for the two proposed architectures, ANN-L16 and ANN-L36, in all three parts of the experiment showed that the different nature of the data set does not affect the complexity of the architecture used. That is, it does not depend on the value of the input values. In the first proposed architecture, ANN-L16, all six input values were used (where four are linearly independent and two linearly dependent), and the MMRE value in all three parts of the experiment is 7.5% of \[\text{OA(ANN}i)=Yi,\text{ where }i=16,i=36. \tag{30}\]4 Using the second architecture, ANN-L36 prim, with four independent input values, the same MMRE value, of 7.5%, was obtained in all three parts of the experiment (Table 7). The error differences in individual parts of the experiment are not more than 0.5%, indicating the proposed model's reliability. The huge values of the correlation coefficients (Pearson's and Spearman's rho) further show the consistency of the actual and estimated values obtained using the proposed models. In the ANN-L36 prim architecture, the Pearson value is 0.983, which indicates an exceptional interrelationship between the observed values (Table 8). Prediction represents the number of projects that have an error less than the set criterion. Prediction can further confirm the validity and reliability of the models used. For all three proposed criteria (PRED (25), PRED (30), and PRED (50)) and in all three parts of the experiment, using both proposed architectures, the value is 100% (Table 9). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & **ANN-L16** & **ANN-L36 prim** & \multirow{2}{*}{**Part of Experiment**} \\ \cline{2-3}  & **MMRE (\%)** & **MMRE (\%)** & \\ \hline \multirow{2}{*}{Dataset\_1} & 6.7 & 7.0 & Training \\  & 7.1 & 7.1 & Testing \\ \multirow{2}{*}{Dataset\_2} & 8.0 & 7.5 & Validation1 \\ \multirow{2}{*}{Dataset\_3} & 8.3 & 8.4 & Validation2 \\ \hline \multicolumn{3}{c}{AVERAGE(MMRE)} & 7.5 & 7.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: MMRE value in all three parts of the experiment (UCP). Figure 8: “Winner” MRE vs. MMRE on the training dataset (ANN-L36 prim). By examining the influence of dependent and independent variables on the change in the MMRE value, it was shown that it is sufficient to use a four-dimensional vector instead of a six-dimensional vector. The error with dependent input values on the four datasets used is between \(-\)0.3 and 0.5, which is less than 1%. The most significant influence is the input value of AUCP (Dataset_3), and the change in the value of MMRE is, in this case, higher by 0.5%.