## 5 Conclusions  
This paper presents a method for predicting the sound quality of the Hy-Vo chain transmission system in the case of small samples. The main conclusions are summarized as follows:  
1. First of all, we conducted the noise acquisition test of the Hy-Vo chain transmission system under different working conditions. Secondly, we evaluated the processed noise samples subjectively and objectively. To solve the problem of small sample prediction, we found and made use of the fuzzy phenomenon in sound quality prediction, and then introduced fuzzy mathematics theory to use a sample generation method called fuzzy generation.
2. After using the fuzzy generation to expand the dataset, we used three different models to test the effect of this new method, namely, the GRNN model, the SVR model, and the RR model. On the new dataset, the prediction accuracy of all three models was significantly improved. The fitting degree and stability of both the GRNN model and the SVR model were higher than before when the membership degree was medium and high. Only the fitting degree and stability of the RR model did not get better or even worse for all membership values.  
To sum up, fuzzy generation is an effective method for small sample sound quality prediction, which solves the problem of low model accuracy and easy overfitting in the case of small samples. Moreover, compared with the performance of different models, the GRNN model is more suitable for sound quality prediction of the Hy-Vo chain transmission system. Therefore, the sound quality prediction method based on fuzzy generation proposed in this paper not only provides a new idea for the research field of chain drive but also puts forward a novel and effective method for the related research of sound quality prediction. It should be pointed out that different types of chain drive systems have different noise characteristics, so the sound quality prediction model proposed in this paper cannot be applied directly. For a specific chain drive system, the process of establishing the sound quality prediction model is as follows: First of all, it is necessary to redesign the noise acquisition test and obtain a new noise sample set. Secondly, the acoustic parameters to be analyzed are selected and calculated, and the testers should be selected according to the application scenario. Then, the objective parameters and subjective evaluation results are analyzed and screened, and the data set for sound quality prediction can be obtained. Finally, the optimal sound quality prediction model is constructed and trained.  
Figure 10: Comparison of RPD.  
**Author Contributions:** Conceptualization, J.L.; Data curation, J.L.; Formal analysis, L.A.; Funding acquisition, Y.C.; Investigation, H.W.; Methodology, J.L. and L.A.; Project administration, Y.C.; Resources, Y.C.; Software, J.L.; Supervision, L.A.; Validation, J.L., L.A. and H.W.; Visualization, J.L.; Writing--original draft, J.L.; Writing--review and editing, J.L. and L.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by [National Natural Science Foundation of China] grant number [51775222] and [Science and Technology Development Project of Jilin Province in China] grant number [20200401136 GX]. And The APC was funded by [51775222].  
**Instructional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.