## 3 Results
### ANN-LM Optimisation Performance Modelling Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Protection Distance km** & **No. of Neurons** & **RBFNN MSE \%** & **GRNN MSE \%** \\ \hline \multirow{5}{*}{10} & 0 & 0.168253 & 0.168258 \\  & 50 & 1.08857 \(\times\) 10\({}^{-15}\) & 1.08860 \(\times\) 10\({}^{-15}\) \\  & 100 & 4.17612 \(\times\) 10\({}^{-18}\) & 5.17622 \(\times\) 10\({}^{-18}\) \\ \cline{1-1}  & \multicolumn{2}{c}{**0.013071**} & **0.438777** \\ \cline{1-1}  & 0 & 0.128807 & 0.138813 \\ \cline{1-1}  & 50 & 4.8190 \(\times\) 10\({}^{-15}\) & 4.8211 \(\times\) 10\({}^{-15}\) \\ \cline{1-1}  & 100 & 2.81559 \(\times\) 10\({}^{-17}\) & 7.74620 \(\times\) 10\({}^{-18}\) \\ \cline{1-1}  & \multicolumn{2}{c}{**0.017157**} & **0.400982** \\ \cline{1-1}  & 0 & 0.168153 & 0.168270 \\ \cline{1-1}  & 50 & 1.88574 \(\times\) 10\({}^{-13}\) & 1.8954 \(\times\) 10\({}^{-13}\) \\ \cline{1-1}  & 100 & 3.61909 \(\times\) 10\({}^{-18}\) & 7.44741 \(\times\) 10\({}^{-18}\) \\ \cline{1-1}  & **0.016235** & **0.471825** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: MSE values for RBFNN and GRNN algorithms. Figure 25: _Cont._  
Figure 25: ANN-LM validation and performance technique for protection distance between 5G-BS and FSS-ES. (**a**) ANN-LM performance training vs. epochs of three protection distances 10, 20, and 35 km. (**b**) Mean squared error performance. (**c**) Error histogram instances. (**d**) Particular correlation coefficient. Table 11 presents MSE values for the RBFNN and GRNN algorithms. The RBFNN algorithm mean squared error (MSE) values for various protection distances (in kilometres) and numbers of neurons are shown in Table 11. The RBFNN technique produced an MSE value of 0.168253 at a protection distance of 10 km when zero neurons were used (0 neurons). The MSE value decreased considerably to 1.08857 \(\times\) 10\({}^{-15}\) when there were 50 neurons added. The MSE value was further decreased to 4.17612 \(\times\) 10\({}^{-18}\) after the number of neurons was increased to 100. This shows the processing by the RBFNN algorithm to produce the MSE values for the specified protection distances and neuronal counts. These data show that by lowering the MSE and producing more precise predictions, increasing the RBFNN algorithm's neuron count can greatly enhance its performance. A further indication that the RBFNN algorithm can handle the data effectively and deliver results efficiently. The GRNN algorithm produced an MSE value of 0.168258 for a protection distance of 10 km. When there were 50 and 100 neurons, the MSE values fell considerably to 1.08860 \(\times\) 10\({}^{-15}\) and 5.17622 \(\times\) 10\({}^{-18}\), respectively. This shows the processing of the GRNN algorithm to compute the MSE values for the given protection distances and neuronal counts. These findings show that increasing the number of neurons in the GRNN algorithm can significantly enhance its performance, as shown by the decline in MSE values. The GRNN algorithm is reasonably efficient in processing the data and generating results. The MSE value at a protection distance of 20 km for the RBFNN algorithm was 0.128807 when no neurons (0 neurons) were used. The MSE values dropped to extremely low levels, measuring 4.8190 \(\times\) 10\({}^{-15}\) and 2.81559 \(\times\) 10\({}^{-17}\), respectively, as the number of neurons reached 50 and 100. This decrease in MSE indicates that the RBFNN algorithm's ability to accurately capture underlying patterns is enhanced by adding more neurons. However, when no neurons were used, the GRNN algorithm at a 20-kilometre protection distance produced an MSE value of 0.138813. The MSE values were 4.8211 \(\times\) 10\({}^{-15}\) and 7.74620 \(\times\) 10\({}^{-18}\), respectively, for 50 and 100 neurons. According to these findings, the GRNN algorithm, despite having somewhat higher MSE values than the RBFNN algorithm, performs similarly in terms of prediction errors. Both the RBFNN and GRNN algorithms show their capacity to obtain low MSE values at a protection distance of 20 km, with variations depending on the number of neurons used.