## 3 Performance Evaluation
### Experimental Results  
The CPU utilization is the number of processor cycles utilized by the process. In our experiment, we simultaneously launched a process of several applications to evaluate the effect of utilization RAM and storage to CPU utilization. In our paper, we analyzed RAM, storage and CPU, which can be updated, modified or configured by any user when some bottlenecks are detected during the system work. The cache has a huge influence on overall performance but we did not consider cache memory because it is an embedded system, which cannot be updated or modified. This section contains a simulation analysis of the proposed models. To demonstrate the performance of neuro-fuzzy logic  
Figure 10: The surface viewer of CPU utilization: (**a**) The Mamdani FIS model; (**b**) the Sugeno FIS model. Figure 9: Rule Viewer showing the defuzzified form of parameters: (**a**) The Mamdani FIS model, the combined result is shown with the red line on the graph, showing the “Middle” status of CPU utilization; (**b**) The Sugeno FIS model outputs a numerical value. models (Mamdani FIS and Sugeno FIS), they were compared with the actual state of CPU utilization obtained from the performance monitoring program. The performance monitor gives only numerical real-time data about the state of each hardware component. Comparing the results of FIS models and the performance monitor gives the opportunity to evaluate the reliability of the FIS models. A twenty-four-second duration state was monitored with various characteristics, such as processor time (CPU), disk time (storage) and paging (RAM). At the same time, acquired data was evaluated by the Mamdani and Sugeno FIS models. The FIS model evaluated the utilization of CPU and performed the results. In Table 4, the first three columns are data obtained via performance monitoring. The remaining columns represent the results of the numerical and linguistic value of the assessment of the Mamdani FIS and Sugeno FIS models. As we see, the numerical results of the Mamdani and Sugeno models is quite close to the actual state of CPU utilization. This means that our proposed Fuzzy inference systems are evaluating the true state of CPU, i.e., the built rule base and trained database sufficiently learned by inference engines. The linguistic results presented by the FIS models are more convenient and provide a demonstrative conclusion to the user about CPU utilization. To analyze the results, we made a graphical visualization. Figure 11a shows the effect of the RAM and storage utilization separately to the CPU utilization at the same time. The line with circles represents dependency on RAM utilization and CPU utilization. The line with squares represents storage and CPU dependency. We can see that the line graph shows that CPU utilization is increased accordingly by increasing RAM and storage utilization, i.e., utilization of these two objects increases or decreases CPU activity. In addition, the graph shows that RAM utilization more significantly affects it compared to storage utilization. Figure 11b presents twenty-four cases of the combined influence of RAM and storage on CPU utilization. The meaning of this graph is the same as analyzing Figure 11a. The graph shows that, in the tested computer, the regularity, influence, and congruence