# Deep Learning Cluster Structures for Management Decisions: The Digital CEO +
## 5 Experimental Results
### Quality of Service Deep Learning Cluster Results (4 \(\times\) 4 Nodes)  
The 4 \(\times\) 4 CPN is simulated with a continuous 380 Cognitive Packet stream. The first 100 packets are used to initialize the CPN network. Goal changes after 40 packets whereas QoS metric changes 2 packets after the new Goal is selected following \(T_{l}\) = 0.99\(T_{l-1}\) + 0.01\(R\) where \(T\) is the Threshold at decision packet \(l\) and \(R\) is the Reward. The QoS DL clusters have been validated with seven different variable Goals for the same Cognitive Packet stream (Table 10). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Cognitive Packet 30**} & \multicolumn{2}{c}{**Cognitive Packet 85**} & \multicolumn{2}{c}{**Cognitive Packet: 148**} \\  & **G: 1.0 \(\times\) D + 0.0 \(\times\) L + 0.0 \(\times\) B** & **G: 0.5 \(\times\) D + 0.5 \(\times\) L + 0.0 \(\times\) B** & **G: 0.3 \(\times\) D + 0.3 \(\times\) L + 0.3 \(\times\) B** \\ \hline Cyber Attack & \(\Delta\) = 0.0 & \(\Delta\) = 0.1 & \(\Delta\) = 0.0 & \(\Delta\) = 0.1 & \(\Delta\) = 0.0 & \(\Delta\) = 0.1 \\ Cyber I\({}_{\rm{cmc}}\) & 5 \(\times\) 10\({}^{-11}\) & 3.4 \(\times\) 10\({}^{-4}\) & 5 \(\times\) 10\({}^{-11}\) & 3.4 \(\times\) 10\({}^{-4}\) & 5 \(\times\) 10\({}^{-11}\) & 3.4 \(\times\) 10\({}^{-4}\) \\ Cyber Y\({}_{\rm{cmc}}\) & 0.9994 & 0.9969 & 0.9994 & 0.9969 & 0.9994 & 0.9969 \\ QoS-Delay I\({}_{\rm{cmc}}\) & 0.6300 & 0.6300 & 0.3150 & 0.3150 & 0.2100 & 0.2100 \\ QoS-Loss I\({}_{\rm{cmc}}\) & 0.0000 & 0.0000 & 0.2625 & 0.2625 & 0.1750 & 0.1750 \\ QoS-Band I\({}_{\rm{cmc}}\) & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.2133 & 0.2133 \\ QoS-Delay Y\({}_{\rm{cmc}}\) & 0.1765 & 0.1765 & 0.3000 & 0.3000 & 0.3913 & 0.3913 \\ QoS-Loss Y\({}_{\rm{cmc}}\) & 0.9994 & 0.9994 & 0.3396 & 0.3396 & 0.4354 & 0.4354 \\ QoS-Band Y\({}_{\rm{cmc}}\) & 0.9994 & 0.9994 & 0.9994 & 0.9994 & 0.3875 & 0.3875 \\ CEO I\({}_{\rm{cmc}}\) & 0.1000 & 0.1000 & 0.1000 & 0.1000 & 0.9000 & 0.9000 \\ CEO V\({}_{\rm{CBomc}}\)\({}^{-}\) (c) & 0.0000 & 0.9999 & 0.0000 & 0.9999 & 0.0000 & 0.9999 \\ CEO Y\({}_{\rm{CBomc}}\) & 0.9994 & 0.5746 & 0.9994 & 0.5746 & 0.9994 & 0.1305 \\ \multirow{2}{*}{\
\begin{tabular}{c} Routing \\ Decision \\ \end{tabular} } & RNN-DL & DL-Delay & RNN-DL & DL-Delay & RNN-DL & DL-Band \\ \multirow{2}{*}{
\begin{tabular}{c} Decision \\ \end{tabular} } & Gate-4 & Gate-2 & Gate-4 & Gate-2 & Gate-4 & Gate-2 \\  & Node 6 & Node 4 & Node 6 & Node 4 & Node 6 & Node 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: DL Management Cluster Validation (3 \(\times\) 3 Nodes). Figure 9: QoS Deep Learning cluster validation (3 \(\times\) 3 Nodes). The average error and learning algorithm iteration values for the QoS and Cyber DL clusters is shown in Table 11.  
The number of updates in the network weights or routing table for the DL cluster and the RNN Reinforcement Learning is shown in Table 12.  
The number of iterations to update RNN-RL is only one whereas DL clusters require approximately 150 iterations as shown in Table 11. The route decision taken by the CEO Management Cluster when the Cyber management cluster has authorized the different Cyber keys are shown in Table 13, for the first Goal only. The results provided by the 4 \(\times\) 4 CPN are similar to the 3 \(\times\) 3 CPN. The first two packets follow the best route whereas the third packet acknowledges the QoS metrics have changed. RNN-RL finds the optimum route after Cognitive Packets explore the network and DL learns the route a Cognitive Packet after. The Reward and Threshold of route decision taken by the CEO Management Cluster when the Cyber management cluster has authorized the different Cyber keys is shown in Figure 10 for the seven different Goals. When the new best route is discovered; the CPN Threshold adapts gradually to the original value.