Chapter 3: Integrated fuzzy reasoning and neural networks to enhance performance
3.6. Effectiveness evaluation simulation of FNNRED and FNNREM
3.6.1.1. Queue control of FNNRED and FNNREM
The Graph of Figure 3.18 shows that FNNRED mechanism has the range (less than 50 packets) smaller the range of FEM mechanism (more than 100 packets). Graph of Figure 3.19 goes on demonstration FNNRED mechanism which is capable of queue control better than FLRED mechanism, although FLRED has rather small range (less than 70 packets).
Figure 3.18. Queue control of FEM and FNNRED mechanisms
Figure 3.19. Queue control of FLRED and FNNRED mechanisms
Similarly, when simulating the mechanisms to improve REM mechanism using fuzzy controller (FUZREM, FLREM, FNNREM), results in Figure 3.20 and Figure 3.21 show that the mechanisms have the rather small range of queue length. Figure 3.20 shows the difference in the queue range between FNNREM mechanism and FUZREM mechanism and Figure 3.21 shows the difference in the queue between FNNREM mechanism and FLREM mechanism.
Figure 3.20. Queue control of FURZEM and FNNREM mechanisms
Figure 3.21. Queue control of FURZEM and FNNREM mechanisms
From the results of foregoing simulation and analysis, it is shown that the queue length control efficiency of the mechanisms is better and better when using adaptive fuzzy controller, FNN to improve the active queue management mechanism.
3.6.1.2. Responsiveness of FNNRED and FNNREM
Responsiveness of queue management mechanisms based on queue length is shown in Figure 3.22 and Figure 3.23. FEM mechanism needs 10 seconds to stabilize the queue while reducing the flow in half (reduction) in the 40th second, this figure is 6 seconds for FLRED and 4 seconds for FNNRED. The result is similar at the 70th seconds, increasing the number of flows to 100 (increasing load), FEM needs 5 seconds for queue stability, FLRED and FNNRED need 3 seconds. On the other hand, in both cases, causing loading fluctuation of the network, the range of the queue length of FNNRED is always lower than FEM and FLRED.
Figure 3.22. Responsiveness of FEM and FNNRED
Figure 3.23. Responsiveness of FLRED and FNNRED
Response time and queue range FNNREM mechanism is always minimum, in both cases of the load decrease and the load increase. This is achieved because FNNREM uses optimal fuzzy control for training and updating the fuzzy system parameters so that the output of the system may reach the most desired values.
Figure 3:24. Responsiveness of FUZREM and FNNREM
Figure 3.25. Responsiveness of FLREM and FNNREM
Basing on the results of the simulation settings and graph figures, it is shown that response time and range of the queue declined as the queue management mechanisms using traditional fuzzy controller, adaptive fuzzy controller and FNN applied in turns at the network nodes in turn applied to improve these mechanisms.
3.6.2. Performance evaluation of FNNRED and FNNREM 3.6.2.1. Packet loss ratio evaluation of FNNRED and FNNREM
Figure 3.26 performs data of Table B.5 and Table B.6 of Appendix B, it shows the packet loss ratio of active queue management mechanisms using fuzzy control. From the graph, it shows that when the queue length in the router increases, the packet loss ratio of mechanisms is reduced and when increasing number of connection flows to the router, the packet loss ratio
increases.
Figure 3.26. Packet loss ratio of mechanisms using fuzzy control
This proves that the packet loss ratio of AQM mechanisms in simulation depends heavily on the fuzzy controllers used. From the graph in Figure 3.26, it is found that when improving in the same traditional mechanism (RED, REM), which mechanism using adaptive fuzzy control (AFC) will have a lower loss ratio compared to the mechanism using traditional fuzzy controller, but it has a packet loss ratio higher than the mechanisms using FNN.
3.6.2.2. Transmission line using level evaluation of FNNRED and FNNREM
Figure 3.27. Transmission line using level of mechanism using fuzzy control
Basing on the graph, it shows the partition of mechanisms on the extent of the transmission line. In both graphs of Figure 3.27, transmission line using level is increasing from group mechanisms using traditional fuzzy controller (such as FEM, FUZREM), followed by a group of mechanisms using AFC like (as FLRED, FLREM) to group of mechanisms using FNN (like FNNRED, FNNREM). This is consistent with the results of theoretical analysis, when AFC using Fuzzy Sugeno with adjustment mechanism of output parameter K and methods of determining Gm sample for target values, and FNN is built from AFC by training to get the value set for the optimized parameter, so that the deviation of the output values of the system compared to the expected values is minimal.