## 4 Sound Quality Prediction  
### Modeling  
To test the effect of fuzzy generation, different models need to be trained on the old and new datasets to compare the advantages and disadvantages of prediction results, and the ratio of training set to test set is 4:1. At the same time, the influence of five membership values on prediction results can be compared. Furthermore, when training with the original dataset, it can be regarded as the case where the membership value is equal to 1. In this paper, we set up different prediction models based on three methods: general regression neural network (GRNN), support vector regression (SVR) model, and ridge regression (RR). The reason why these three methods are chosen is that they all have their specific advantages: GRNN has a strong nonlinear mapping ability and can handle the nonlinear relationship between acoustic parameters and evaluation scores. SVR is more concerned with determining the best-fit line and limiting the error to a certain threshold. There is often a certain correlation between objective acoustic parameters, and RR can handle the strong correlation between variables to improve the stability of the model. (1) GRNN model  
General regression neural network (GRNN) is a type of neural network based on radial basis function (RBF), which is mainly used for regression analysis and function approximation. Generally speaking, GRNN mainly has two biggest advantages, on the one hand, the structure is simple, including the input layer, mode layer, summation layer, and output layer. On the other hand, there is only one key parameter called the smoothing factor, a small smoothing factor will cause the network to overfit, while a large smoothing factor may cause the network to underfit [17; 18; 19]. To find the optimal smoothing factor, we choose to use the particle swarm optimization (PSO) algorithm. In this paper, the number of particles is set to 30 and the maximum number of iterations is set to 20. The optimization results of the smoothing factor are shown in the following Table 6.  
(2) SVR model  
Support vector regression (SVR) is a regression version of support vector machines (SVM), but unlike SVM classification, where the goal is to find a hyperplane that maximizes the interval, SVR aims to find a hyperplane so that most of the data points fall within the tolerance range of this hyperplane. To perform nonlinear regression in high-dimensional space, SVR can use different kernel functions, such as linear kernel, polynomial kernel, RBF kernel, and sigmoid kernel. There are two key parameters \(c\) and \(g\) in SVR, \(c\) is called the regularization parameter and \(g\) is called the kernel parameter. The size of the \(c\) value determines the tolerance of the model to error, and the size of the \(g\) value determines the influence range of each training sample [20; 21; 22]. We establish an SVR model with the RBF kernel in this research and use PSO to obtain the best \(c\) value and best \(g\) value. The initial conditions of PSO are set: the number of particles is 30 and the maximum number of iterations is 20. The final optimization results are shown in Table 7. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Membership Value** & **1** & **0.9** & **0.7** & **0.5** & **0.3** & **0.1** \\ \hline Smoothing factor & 0.1607 & 0.0121 & 0.0509 & 0.0103 & 0.1314 & 0.1462 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Optimal smoothing factor. (3) RR model  
Ridge regression (RR), also known as Tikhonov regularization, is a regularized version of linear regression. It solves the multicollinearity problem in linear regression by adding a regular term to the loss function, thereby improving the stability and predictive power of the model. RR has a parameter \(\lambda\) that controls the strength of the regularization, and when \(\lambda=0\), RR is an ordinary linear regression. As \(\lambda\) increases, the intensity of regularization increases and the complexity of the model decreases.