## 5 Results and Discussion
### Multilayer Perceptron Model (MLP)
Figure 6 shows the two-layer architecture of the MLP model which achieved the above performance, where the synaptic connections, which are represented by dark blue, have weight factors less than zero, while the lighter color represents positive values of the weight factors. The metaparameters of multilayer perceptron training do not change during the testing of different models. It is specified that the adjustment of the weighting factors is performed after processing each individual input/output vector from the training set (individual or _online_ learning). The training algorithm is backpropagation that implements the gradient descent method. The _Initial Learning Rate_ has a great influence on the convergence of the algorithm and its value is usually chosen from the range of \(10^{-6}\) to 1. A higher learning rate accelerates the training process of the MLP model, but negatively affects its prediction performance. The initial value of the learning rate gradually decreases in each epoch, which represents one passage of a complete set of training data through the training algorithm, to a certain lower limit. The IBM SPSS technology specifies a _default_ value of the initial learning rate of 0.4 while its lower limit is 0.001. The maximum number of epochs is determined automatically. The following part of this section provides detailed training and testing results for the selected final MLP model with the lowest individual RE values for the two dependent variables (RE = 0.098 for the _Cell Downlink Average Throughput_ variable and RE = 0.115 for the _Average User Downlink Throughput_ variable). Based on the results shown in Table 9,  
Figure 6: Multilayer architecture of the created model MLP. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \begin{tabular}{c} **The Ratio of** \\ **Data Sets for** \\ **Training and** **Testing** \\ \end{tabular} & \begin{tabular}{c} **No of** \\ **Neurons in** \\ **Hidden Layers** \\ \end{tabular} & \begin{tabular}{c} **Output** \\ **Function of** \\ **Neurons in** \\ **Hidden Layers** \\ \end{tabular} & \
\begin{tabular}{c} **Cell Downlink Average** \\ **Throughput** \\ **Measurements** \\ \end{tabular} &
\begin{tabular}{c} **Average User Downlink** \\ **Throughput** \\ **Measurements** \\ \end{tabular} \\ \hline
30\%:70\% & 30, 30 & Hyp. Tg. & 0.120 & 0.113 & 0.122 & 0.118 & 0.140 & 0.134 & 0.133 & 0.136 \\
40\%:60\% & 30, 30 & Hyp. Tg. & 0.117 & 0.111 & 0.111 & 0.113 & 0.140 & 0.131 & 0.129 & 0.133 \\
50\%:50\% & 20, 20 & Hyp. Tg. & 0.112 & 0.120 & 0.114 & 0.115 & 0.128 & 0.124 & 0.138 & 0.130 \\
60\%:40\% & 30, 30 & Hyp. Tg. & 0.105 & 0.118 & 0.111 & 0.111 & & & & \\
70\%:30\% & 20, 20 & Hyp. Tg. & 0.107 & 0.098 & 0.107 & 0.104 & 0.124 & 0.115 & 0.121 & 0.120 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Least average values of RE by examined ratios of training data set and test data set. generated by IBM SPSS technology, it can be seen that the relative error, i.e., the degree of accuracy of the model over the training data, for the variable _Cell Downlink Average Throughput_ is 89.8% (RE = 0.102) and is slightly lower in relation to the degree of accuracy for the test set (the degree of accuracy is 90.2%, RE = 0.098), while for the variable _Average User Downlink Throughput_, the degree of accuracy over the training data is 88.6% (RE = 0.114), and for testing 88.5% (RE = 0.115). During the training process, the goal is to minimize the _error function_ which, in the observed case, is represented by the _Sum of Squares Error_ (SSE), and is calculated according to the equation:  
\[SSE=\sum_{i=1}^{N}(RV_{i}-PV_{i})^{2} \tag{6}\]  
where _RV_ (_Real Value_) represents the actual value of the observed dependent variable, _PV_ (_Predicted Value_) represents the prediction value of the observed dependent variable, and \(N\) is the number of all measurements.