## 4 Model Lightweight Algorithm
### Response Surface Analysis Fitting and Precision Testing  
If there are too few provided parameter points or selection errors in the experimental design; the estimated model terms may be confounded in later analysis. Therefore, considering the interaction effects of the two factors, it is important to first determine whether the selected design can sufficiently estimate the coefficients of the desired model, as shown in Table 3.  
From Table 3, it can be seen that regardless of considering a single factor or the interaction effects of two factors, the estimated standard deviation of the same type of coefficient is highly similar. The variance inflation factors are 1.0 and 1.05, indicating that there are good orthogonal relationships between the selected parameters to be optimized. This satisfies the correlation between the regression coefficients, and the influence of each independent variable and the target variable can be accurately estimated and explained. Thus, the prediction effect will be more reliable and accurate. After comparison, it was found that the probability calculation of the model with a standard deviation of 2.0 is superior to those with standard deviations of 0.5 and 1.0. Therefore, a quadratic polynomial was chosen for data regression. Due to space limitations, only the response surface results of the five objective functions for parameters A and B are presented in Figure 7.  
Figure 6: The design test operating conditions. Figure 7: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Experimental** \\ **Design Elements** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Variance Inflation** \\ **Factor** \\ \end{tabular} } & \multicolumn{2}{c}{**Design Assessment Probability Calculation**} \\ \cline{4-5}  & & & **0.5 Standard** & **1.0 Standard** & **2.0 Standard** \\  & & & **Deviation** & **Deviation** & **Deviation** \\ \hline
**A** & 0.20 & 1.00 & 20.9\% & 63.0\% & 99.5\% \\ \hline
**B** & 0.20 & 1.00 & 20.9\% & 63.0\% & 99.5\% \\ \hline
**C** & 0.20 & 1.00 & 20.9\% & 63.0\% & 99.5\% \\ \hline
**D** & 0.20 & 1.00 & 20.9\% & 63.0\% & 99.5\% \\ \hline
**AB** & 0.25 & 1.00 & 15.5\% & 46.5\% & 96.2\% \\ \hline
**AC** & 0.25 & 1.00 & 15.5\% & 46.5\% & 96.2\% \\ \hline
**AD** & 0.25 & 1.00 & 15.5\% & 46.5\% & 96.2\% \\ \hline
**BC** & 0.25 & 1.00 & 15.5\% & 46.5\% & 96.2\% \\ \hline
**BD** & 0.25 & 1.00 & 15.5\% & 46.5\% & 96.2\% \\ \hline
**CD** & 0.25 & 1.00 & 15.5\% & 46.5\% & 96.2\% \\ \hline
**A\({}^{2}\)** & 0.19 & 1.05 & 68.7\% & 99.8\% & 99.9\% \\ \hline
**B\({}^{2}\)** & 0.19 & 1.05 & 68.7\% & 99.8\% & 99.9\% \\ \hline
**C\({}^{2}\)** & 0.19 & 1.05 & 68.7\% & 99.8\% & 99.9\% \\ \hline
**D\({}^{2}\)** & 0.19 & 1.05 & 68.7\% & 99.8\% & 99.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The computational probability evaluation. The significance of optimization parameters A-D to target functions \(R_{1}\)-\(R_{5}\) to be corrected was analyzed using the F-test method. Assuming the model contains \(m\) parameters, the statistical variance was calculated to ensure the significance of \((m+1)\) parameters. \[F_{m+1}=\frac{(S_{m}-S_{m+1})/(\eta_{m}-\eta_{m+1})}{S_{m+1}/\eta_{m+1}}, \tag{9}\]  
where \(S_{m}\) and \(S_{m+1}\) are the sum-of-squares errors in the response surface equation for the \(m\)-th parameter and the \((m+1)\)-th parameter, respectively, and \(\eta_{m}\) and \(\eta_{m+1}\) are the degrees of freedom for the \(m\)-th parameter and the \((m+1)\)-th parameter, respectively. The criterion for the significance test of the parameters is as follows: given a significance level \(\alpha\), when \(F_{m+1}>F_{1-\alpha}(1,n-m-1)\), the \((m+1)\)-th parameter has higher significance and should be included in the response surface model; otherwise, it will be removed.