## 7 Results and Analysis
Moreover, the mode train FIS can use the optimizer methods as either a backpropagation alone or a hybrid, which is a combination of backpropagation and the least-squares-type method to allow for the fuzzy learn from the data. The number of outputs is only one, and the grid partition method is used in establishing the rule base relationship between the input and output variable. During training with 150 epochs, the MF of d\({}_{\text{sigmf}}\) and p\({}_{\text{sigmf}}\) perform the most effectively with minimum RMSE under linear MF type output  
Figure 12: Trained main ANFIS of indexes and output of risk (Matlab’s rule viewer). Figure 13: Using the Sugeno-type FIS method in the proposed method. with training hybrid method (see Table 6). The ANFIS models have shown significant performance variations against the evaluation criteria in terms of error and types of membership functions and the method of outputs. From all the values of RMSE, it appears that the ANFIS models are sensitive in different MFs and datasets, so the model can be learning content for the risk in the stations and provide real-time protection. The type of MF inputs, data and MF type outputs are significant for the design of the ANFIS architecture. In the future, a variety of different and long training and testing datasets will be beneficial for conducting extensive research to cover all the dynamic changes in the crowds and covering all different possibilities of risk over a long time to enhance system performance. Moreover, it has been shown that the increase in epoch numbers for a training dataset does not necessarily develop model performance significantly. The motivations behind our design of an adaptive learner model was to support the framework (Section 3: intelligent system for managing risks (ISFMR)) throughout the learning activity in real-time to capture the risk of crowding by the predictor of the passenger flow in the station and that presented that the learning application is functional and can be a reasoning tool for managing dynamic risks. By including the ANFIS output in the system, the risk level for overcrowding can be predicted. In addition, the graph of the training error is presented in Figure 8:0 The model has been set up to train steps of 150 epochs with an addition of the required number of epochs, error diminishes, and error fluctuations to reach a steady state. Clearly, the small amount of training data is a significant performance variable. Moreover, one benefit is that the intended ANFIS example does not require time-consuming iterative training. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Number of**} & \multicolumn{2}{c}{**Training RMSE**} & \multicolumn{2}{c}{**Testing RMSE**} \\ \cline{3-6}
**ANFIS Info** & **MF** & **Nonlinear** & **Back** & **Propagation** & **Hybrid** & **Back** & **Propagation** & **Hybrid** \\ \hline \multirow{6}{*}{Grid} & trimf & 24 & 0.073364 & 0.000119 & 0.214272 & 0.234563 \\  & trapmf & 32 & 0.064496 & 0.000061 & 0.152923 & 0.771647 \\  & gbellmf & 24 & 0.063835 & 0.000001 & 0.100628 & 0.103621 \\  & gaussmf & 16 & 0.063897 & 0.000001 & 0.176282 & 0.079462 \\  & Gauss2 mf & 32 & 0.074480 & 0.000001 & 0.136584 & 0.87736 \\  & pimf & 32 & 0.058077 & 0.000061 & 0.19069 & 3.89485 \\  & dsigmf & 32 & 0.071541 & 0.000000 & 0.117291 & 0.726872 \\  & psigmf & 32 & 0.039306 & 0.000000 & 0.717619 & 0.720104 \\  & Number of linear parameters & & & 48 & & \\  & Number of Epochs & & & 150 & & \\  & Number of nodes & & & 53 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: RMSE results for different membership functions (MFs) with Membership Functions Number (4*4) from the training and testing datasets. Figure 14: Trend of errors of the trained fuzzy system. The trend of errors of the trained fuzzy system, an example of the increase in the RMSE with training and the output of the ANFIS paradigm, are shown in Figure 15. This shows that the predicted short-term risk level for both the training data and the FIS output are in excellent agreement with the exact results.