## 4 Experiments and Result Analysis
### Experiment Method and Result Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**P** & **C** & **2** & **3** & **4** & **5** & **6** \\ \hline
2 & 0 & 0 & 0 & 0 & 0 \\ \hline
3 & 0.0201 & 0.0204 & 0.0203 & 0.0202 & 0.0203 \\ \hline
4 & 0.3194 & 0.3221 & 0.3302 & 0.3315 & 0.3295 \\ \hline
5 & 0.4548 & 0.4587 & 0.4548 & 0.4488 & 0.4637 \\ \hline
6 & 0.5125 & 0.5149 & 0.5208 & 0.5184 & 0.5196 \\ \hline \end{tabular}
\end{table} 
Table 3: Prediction performance CGK-based fuzzy granular model of the aggregated structure that generated contexts uniformly. Table 5 summarizes the prediction performance of the optimized CGK-based fuzzy granular model that generated contexts uniformly. Table 6 summarizes the prediction performance of the optimized CGK-based fuzzy granular model of the aggregated structure that generated contexts uniformly. Table 7 shows how the contexts in the output space of the optimized CGK-based fuzzy granular model were optimized from the existing shape. Table 8 shows how the contexts in the output space of the optimized CGK-based fuzzy granular model of the aggregated structure were optimized from the current shape. Here, \(p_{13}\) shows the upper limit of the first context and \(p_{21}\) shows the lower limit of the second context. Because the upper and lower limits of each context were set to the optimal values, the PIs of 0.478  
Table 9 summarizes the results of the concrete compressive strength prediction experiment. The tables show the PI was high when the contexts were generated uniformly in every model. The validation PI of the CGK-based fuzzy granular model was 0.470 when six contexts and four clusters were used. The validation PI of the CGK-based fuzzy granular model of the aggregated structure was 0.5208 when six contexts and four clusters were used. The PIs of the optimized CGK-based fuzzy granular model and the optimized CGK-based fuzzy granular model of the aggregated structure proposed in this study were 0.4781 and 0.5386, respectively, when six contexts and six clusters were used. Therefore, these results confirmed that the proposed models show higher performance than the conventional fuzzy granular model. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & \(p_{13}\) & \(p_{21}\) & \(p_{23}\) & \(p_{31}\) & \(p_{33}\) & \(p_{41}\) & \(p_{43}\) & \(p_{51}\) & \(p_{53}\) & \(p_{61}\) \\ \hline Existing & 17.8 & 2.3 & 33.3 & 17.8 & 48.9 & 33.3 & 64.4 & 48.9 & 79.9 & 64.4 \\ \hline Optimal & 17.7 & 1.6 & 33.2 & 18.2 & 48.2 & 64 & 64 & 49.9 & 80.6 & 65.1 \\ \hline \end{tabular}
\end{table} 
Table 7: Updated values after context optimization of the optimized CGK-based fuzzy granular model. Figure 13: Comparison between the output of the optimized CGK-based fuzzy granular model and the real output. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & \(p_{13}\) & \(p_{21}\) & \(p_{23}\) & \(p_{31}\) & \(p_{33}\) & \(p_{41}\) & \(p_{43}\) & \(p_{51}\) & \(p_{53}\) & \(p_{61}\) \\ \hline Existing & 17.8 & 2.3 & 33.3 & 17.8 & 48.9 & 33.3 & 64.4 & 48.9 & 79.9 & 64.4 \\ \hline Optimal & 17.4 & 3.3 & 33.2 & 18.5 & 47.9 & 34 & 64.5 & 49.6 & 78.9 & 65.1 \\ \hline \end{tabular}
\end{table} 
Table 8: Updated value after context optimization of the optimized CGK-based fuzzy granular model of the aggregated structure. Figure 14: PI of the optimized CGK-based fuzzy granular model. Figure 15: Comparison of the output of the optimized CGK-based fuzzy granular model of the aggregated structure and the real output. Table 10 shows the result of the Boston House Prices prediction experiment. As shown in the table, the validation PI of the CGK-based fuzzy granular model was 0.5502 when the contexts were generated uniformly, and six contexts and five clusters were used. The PI of the CGK-based fuzzy granular model of the aggregated structure was 0.5870 when the contexts were generated flexibly, and six contexts and three clusters were used. The PI of the optimized CGK-based fuzzy granular model proposed was 0.5733 when the contexts were generated uniformly, and six contexts and five clusters were used.