## 4 Result and Discussion
### Comparison of Reverse Prediction Results  
Using the quality indicators of the intended finished board as input data and the production parameters as output data, each prediction model was run 20 times, and the average value was taken. The evaluation indicators for the reverse prediction of each model are shown in Table 4 and the reverse prediction performance of each model is shown in Figure 6:  
From the evaluation indexes of the forward prediction model, it can be seen that the average \(R^{2}\) value of the cHGWOSCA-SVR forward prediction model is 0.9553 which is closer to 1, the MAE and MSE values are lower compared to other models and the average running time of the algorithm is 85.929 s. Compared with other prediction models, the cHGWOSCA-SVR forward prediction model has higher prediction accuracy and minimizes the error value of the algorithm.  
In summary, the cHGWOSCA-SVR bi-directional prediction model used in this study has good prediction ability in both the forward and reverse prediction and has higher accuracy and a lower prediction error than the traditional SVR prediction model. Meanwhile, we can also see that although the SVR algorithm has a strong nonlinear fitting ability, when facing highly nonlinear problems, especially when the number of samples is not large enough, the SVR model cannot achieve high prediction accuracy. Intelligent algorithms, especially combined intelligent algorithms, can be used to optimize the parameters of the traditional SVR regression prediction algorithms, which is obviously a more effective  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Predictive Indicators** & \(\mathbf{R^{2}}\) & \(\mathbf{R^{2}}\)**on Average** & **MAE** & **MSE** & **t (s)** \\ \hline \multirow{4}{*}{cHGWOSCA-SCR} & Density & 0.9520 & & 0.0457 & 0.0025 & \\  & MC & 0.9717 & & 1.1573 & 1.8628 & \\  & GS & 0.9573 & & 0.5907 & 0.4038 & \\  & SD & 0.9217 & & 0.4064 & 0.2044 & \\  & PHT & 0.9697 & & 0.4797 & 0.3306 & \\  & ET & 0.9597 & & 1.5058 & 2.6625 & \\ \hline \multirow{4}{*}{GWO-SVR} & Density & 0.9231 & & 0.0607 & 0.0041 & \\  & MC & 0.9538 & & 1.5573 & 2.9488 & \\  & GS & 0.9296 & & 0.7907 & 0.6800 & \\  & SD & 0.8898 & & 0.5064 & 0.2957 & \\  & PHT & 0.9474 & & 0.6797 & 0.5625 & \\  & ET & 0.9497 & & 1.7050 & 3.3023 & \\ \hline \multirow{4}{*}{PSO-SVR} & Density & 0.8862 & & 0.0758 & 0.0062 & \\  & MC & 0.9272 & & 2.0347 & 4.6909 & \\ \cline{1-1}  & GS & 0.9129 & & 0.8923 & 0.8530 & \\ \cline{1-1}  & SD & 0.8546 & & 0.6065 & 0.4070 & \\ \cline{1-1}  & PHT & 0.9202 & & 0.8798 & 0.8746 & \\ \cline{1-1}  & ET & 0.9387 & & 1.9058 & 4.0273 & \\ \hline \multirow{4}{*}{SVR} & Density & 0.6782 & & 0.0903 & 0.0146 & \\ \cline{1-1}  & MC & 0.4499 & & 4.0051 & 26.5330 & \\ \cline{1-1}  & GS & 0.5056 & & 1.4446 & 3.6079 & \\ \cline{1-1}  & SD & 0.4531 & & 0.7914 & 1.0604 & \\ \cline{1-1}  & PHT & 0.3758 & & 1.6828 & 4.6956 & \\ \cline{1-1}  & ET & 0.3768 & & 4.1803 & 28.9647 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of reverse prediction results among algorithms.  
way. [PERSON] et al. [17], have also used the improved SVR prediction algorithm (IPSO-SVR) to establish a bi-directional prediction model of production parameters and quality and energy consumption of fibers in the fiberboard production process, and have achieved a higher prediction accuracy relative to the traditional SVR prediction model.