## 3 Results
### Experimental Results with ANFIS FCM  
The ANFIS FCM method, like the methods shown above, evaluated multiple ANFIS models for a number of previous samples (N = 2, 3, 4, 5). Table 3 summarizes these results. ANFIS models are shown in Figure 14a-d. The network architecture is directly influenced by the number of clusters. Figure 10 from the ANFIS subtractive clustering method shows that the architectures are comparable. In fact, the method is very similar to the one described in the previous paragraph. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Model Parameters**} & \multicolumn{2}{c}{**Training**} & \multicolumn{4}{c}{**Training**} & \multicolumn{4}{c}{**Training**} & \multicolumn{4}{c}{**Testing**} \\ \hline
**Nr. Crt** & **N** & **C** & \multicolumn{2}{c}{**Time**} & \multicolumn{2}{c}{**MSE**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**R**} & \multicolumn{2}{c}{**MSE**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**R**} \\ \hline
1 & 2 & 2 & 0.18246 & 0.000212 & 0.014566 & 0.971 & 0.000163 & 0.012774 & 0.95502 \\ \hline
2 & 2 & 3 & 0.26939 & 0.000219 & 0.014804 & 0.97003 & 0.000152 & 0.012332 & 0.95856 \\ \hline
3 & 2 & 4 & 0.36268 & 0.000213 & 0.014606 & 0.97083 & 0.000154 & 0.012402 & 0.95787 \\ \hline
4 & 2 & 5 & 0.4642 & 0.00022 & 0.014847 & 0.96985 & 0.000146 & 0.012062 & 0.96012 \\ \hline
5 & 2 & 6 & 0.56686 & 0.000204 & 0.014285 & 0.97212 & 0.000151 & 0.012291 & 0.95913 \\ \hline
6 & 2 & 7 & 0.68551 & 0.000199 & 0.014095 & 0.97287 & 0.000161 & 0.012679 & 0.95627 \\ \hline
7 & 2 & 9 & 0.94153 & 0.000191 & 0.013825 & 0.97391 & 0.000146 & 0.012074 & 0.95988 \\ \hline
8 & 2 & 10 & 1.0583 & 0.000186 & 0.013642 & 0.97461 & 0.00017 & 0.01305 & 0.95276 \\ \hline
9 & 2 & 11 & 1.1927 & 0.000184 & 0.013553 & 0.97494 & 0.000155 & 0.012437 & 0.95739 \\ \hline
10 & 2 & 12 & 1.325 & 0.000183 & 0.013528 & 0.97504 & 0.000157 & 0.012519 & 0.95648 \\ \hline
11 & 2 & 14 & 1.6644 & 0.00018 & 0.013416 & 0.97545 & 0.000174 & 0.013174 & 0.95164 \\ \hline
12 & 2 & 15 & 1.8287 & 0.000176 & 0.01326 & 0.97603 & 0.000194 & 0.013933 & 0.94554 \\ \hline
13 & 3 & 2 & 0.25381 & 0.000226 & 0.015024 & 0.96906 & 0.000145 & 0.012034 & 0.96073 \\ \hline
14 & 3 & 3 & 0.38316 & 0.00021 & 0.014508 & 0.97118 & 0.000148 & 0.012178 & 0.96031 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of modelling and prediction using the ANFIS FCM. Figure 13: Regression coefficients for the training and testing phase for modelling with ANFIS subtractive clustering method. The number of clusters in this architecture, which is also based on the clustering technique, is specified at the beginning, and is, therefore, not determined by other parameters such as the radius of influence. The network design is similar to the subtractive clustering technique in that it is based on the same principles. Similarly, the network's architecture is highly dependent on the number of previous training samples and the number of clusters. As compared to the subtractive clustering method, it is clear that the two methods are very close. This is because, in addition to the number of previous samples, the model contains a parameter with a very similar influence: in the FCM method, this parameter is the number of clusters, which is equal to the number of neurons in the rule layer, and thus to the number of rules. While the previous method used the term \"influence radius\", this term actually refers to the number of clusters (or rules). When the structures of the models for the two methods are compared, as shown in Figures 10 and 14, as well as in Tables 2 and 3, it is clear that the structures are very similar and that some of the parameters are identical. However, when either of the ANFIS methods--subtractive clustering or ANFIS FCM--is compared to the method based on input space partitioning, in which the ANFIS model's parameter is the number of fuzzy sets for each input, it is found that there is no similarity between the models.