## 5 Results  
According to the dataset division into five folds, as discussed in the previous section, it was assumed that the structures of the developed EoNN models would consist of five ANN multilayer perceptron networks, hereinafter referred to as MLP, each with one hidden layer. The selection of each of the five member networks was preceded by training 100 different ANN of MLP types for each fold of \(L\) and \(V\) subsets. It was assumed that the five member networks may vary in terms of their structure or activation functions. (The \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \(p\) & \(y\) & \(x_{1}\) & \(x_{2}\) & \(x_{3}\) & \(x_{4}\) & \(x_{5}\) & \(x_{6}\) & \(x_{7}\) & \(x_{8}\) & \(x_{9}\) & \(x_{10}\) & \(x_{11}\) & \(x_{12}\) \\ \hline
6 & 335.99 & 0.90 & 0.10 & 0.26 & 0.10 & 0.56 & 0.10 & 0.37 & 0.10 & 0.10 & 0.10 & 0.11 & 0.35 \\
14 & 466.37 & 0.10 & 0.13 & 0.26 & 0.30 & 0.44 & 0.10 & 0.90 & 0.19 & 0.10 & 0.12 & 0.15 & 0.13 \\
19 & 534.00 & 0.10 & 0.13 & 0.26 & 0.10 & 0.83 & 0.10 & 0.37 & 0.10 & 0.10 & 0.14 & 0.10 & 0.10 \\
25 & 619.74 & 0.10 & 0.11 & 0.42 & 0.30 & 0.65 & 0.10 & 0.10 & 0.19 & 0.16 & 0.10 & 0.15 & 0.90 \\
40 & 842.48 & 0.90 & 0.12 & 0.26 & 0.10 & 0.52 & 0.10 & 0.37 & 0.19 & 0.12 & 0.13 & 0.11 & 0.35 \\
47 & 987.58 & 0.90 & 0.12 & 0.74 & 0.50 & 0.38 & 0.90 & 0.37 & 0.19 & 0.22 & 0.12 & 0.14 & 0.35 \\
49 & 1043.62 & 0.10 & 0.14 & 0.42 & 0.30 & 0.52 & 0.90 & 0.10 & 0.28 & 0.12 & 0.10 & 0.22 & 0.90 \\
64 & 1642.20 & 0.10 & 0.19 & 0.26 & 0.30 & 0.47 & 0.10 & 0.90 & 0.41 & 0.14 & 0.11 & 0.36 & 0.13 \\
76 & 2200.33 & 0.10 & 0.17 & 0.42 & 0.30 & 0.56 & 0.90 & 0.10 & 0.28 & 0.42 & 0.11 & 0.30 & 0.90 \\
88 & 2668.12 & 0.90 & 0.14 & 0.74 & 0.50 & 0.42 & 0.10 & 0.37 & 0.14 & 0.58 & 0.11 & 0.23 & 0.21 \\
101 & 3690.71 & 0.10 & 0.90 & 0.10 & 0.10 & 0.36 & 0.10 & 0.90 & 0.10 & 0.10 & 0.90 & 0.45 & 0.46 \\
110 & 4515.71 & 0.90 & 0.17 & 0.74 & 0.50 & 0.63 & 0.10 & 0.37 & 0.32 & 0.14 & 0.12 & 0.27 & 0.76 \\
119 & 5768.77 & 0.10 & 0.21 & 0.42 & 0.30 & 0.51 & 0.10 & 0.63 & 0.68 & 0.52 & 0.11 & 0.41 & 0.49 \\
123 & 7102.82 & 0.10 & 0.29 & 0.26 & 0.30 & 0.81 & 0.90 & 0.37 & 0.59 & 0.78 & 0.11 & 0.67 & 0.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Sample of dependent and independent variables values as prepared for computations and computer simulations of ANN. general structure of the considered MLP networks was 12-H-1, including an input layer with 12 neurons reflecting the number of independent variables, a hidden layer with a varying number of neurons symbolized by H, and an output layer with 1 neuron). Possible activation functions in both the hidden and output layers included sigmoid, hyperbolic tangent, exponential, or linear functions. All networks were trained using the quick and effective Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. The selection of ensemble members from the 100 candidate networks was based on the analysis of network performance. The final choice depended on the following:  
* High values of the coefficient \(R\), reflecting the correlation between the predicted and actual values of the dependent variable for learning, validating, and testing subsets;
* Close values of the \(\frac{1}{2}\mathit{MSE}\) error measures computed for learning, validating, and testing subsets, denoting equivalence of the three processes;
* Analysis of \(\mathit{MAPE}\) and \(\mathit{RMSE}\) values, along with the distribution analysis of PE error measures. The characteristics of the ANN selected to be the members of the ensemble EoNN are presented in Table 7. (The subscripts in the table stand for the successive folds of L and V subsets--e.g., F1 for fold 1). The five selected ANNs became EoNN members and provided level-0 predictions of \(\mathit{UCC}\) values, that is, updated severage project construction costs.