## 4 Model Validation
### Model Result and Analysis
#### 4.3.3 Statistical Relevance Analysis  
In the RBF neural network constructed in the paper, we expand the 64 samples in the dataset to 200 and perform a test. The test result is good. But in order to guarantee the statistical relevance of the conclusions, 100 additional experimental validations of the constructed model are conducted, give the relatively limited data set employed in this study. The statistical relevance of the model is tested by means of a systematic adjustment of the sample size. Specifically, 50% of the total 200 samples are selected as the training samples, and 10 independent randomized experiments are conducted, with R\({}^{2}\)-values for both the training and testing sets yielded for each. This is conducted in order to assess the reliability of the model. Subsequently, the proportion of the training sample is increased in increments of 5% from 50% to 55%. 10 independent randomized experiments are conducted for each increment, and the results obtained are recorded in Table 6. Subsequently, 60% of the sample is designated as the training set, while the remaining 40% is designated as the testing set for the experiment. Subsequently, the proportion of the testing set samples is increased gradually from 50% to 95%. Ten independent sets of experiments are conducted for each incremental increase in the training set samples until all 100 experiments are completed. The results of the experiment are presented in Table 6.  
It is worth noting that in each experiment, the training and testing samples are randomly selected from the overall sample, which results in different item sizes and durations in each experiment. This means that the input variables are different combinations of values, as a way to ensure that different input variables do not affect the training results of the model. In order to elucidate the effect of variation in the training sample set on the accuracy of the training results, we also statistically analyzed the composition of the training set projects in each experiment. This includes the number of items of different sizes and the number of items of different durations. The specific data are shown in Table 6. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Data Packet**} & \multicolumn{3}{c}{**SPI(t2)**} & \multicolumn{3}{c}{**SPI(t3)**} \\ \cline{2-6}  & **Real Value** & **Forecast Value** & **Relative Error** & **Real Value** & **Forecast Value** & **Relative Error** \\ \hline
1 & 0.7597 & 0.769 & 0.0122 & 1 & 0.988 & 0.0120 \\
2 & 0.7857 & 0.7895 & 0.0048 & 1 & 0.9785 & 0.0215 \\
3 & 0.6711 & 0.6538 & 0.0258 & 1 & 0.9729 & 0.0271 \\
4 & 0.5393 & 0.5576 & 0.0339 & 1 & 0.9565 & 0.0344 \\
5 & 0.6285 & 0.6385 & 0.0159 & 1 & 0.9645 & 0.0355 \\
6 & 0.8425 & 0.8506 & 0.0096 & 1 & 1.004 & 0.0040 \\
7 & 0.7591 & 0.7695 & 0.0137 & 1 & 0.9818 & 0.0182 \\
8 & 0.3844 & 0.3867 & 0.0060 & 1 & 0.9824 & 0.0176 \\
9 & 0.879 & 0.8824 & 0.0039 & 1 & 0.9904 & 0.0096 \\
10 & 0.9881 & 0.9857 & 0.0024 & 1 & 1.01 & 0.0100 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A comparison of the results of the two groups of performance indicators. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Basis for Grouping**}} & \multicolumn{10}{c}{**Number of Projects with BAC in the**} & \multicolumn{1}{c}{**Number of Projects with PD in the Following Ranges**} \\ \cline{3-14}  & **Percentage of** & **Percentage of** & **SPI(q)** & **SPI(q)** & **SPI(q)** & **Following Ranges (Test0 Unit C)** & & **(Test0 Unit Days)** & \\ \cline{2-14}  & **Training Sets (\%)** & **Testing Sets (\%)** & & & **[0, 50 w]** & **[00 w]** & **[00 w]** & **[00 w]** & **[0, 100]** & **[100, 200]** & **[200, 300]** & **[300, 1000]** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 6: 100 groups of comparative experiments.