# Robust Grey Relational Analysis-Based Accuracy Evaluation Method
## 5 Conclusions  
In this paper, in response to the phenomenon of the poor robustness of grey correlation analysis method in dealing with the accuracy assessment of noisy data, the interpolation method guided by the expected penetration rate is introduced, and on the basis of not changing the shape of the curve of the original data series, the weight of the data distance for the calculation of the grey correlation analysis is indirectly increased by increasing the number of data points within the range of the difference of the distance of a small distance, and the indicator of the acceptable degree of bias p is proposed to be the threshold of the distance of the data. On the basis of taking the absolute value of the difference in the grey correlation analysis interpolation, the weight correction after judging the tolerance of perturbation is added, and the grey correlation is mapped to the accuracy by constructing the grey correlation-mean square, distance-accuracy model. The grey correlation coefficient of individual data points is corrected in the microcosm, and the weight of the data point distance is increased in the macrocosm, which effectively improves the anti-noise performance and anti-perturbation performance of the evaluation results and helps to accurately and clearly reflect the accuracy of the simulation model. This method may have potential applications in many more areas, such as neural network modeling, quality assessment for time series data, and assisted assessment of calibration quality for multi-sensor data alignment calibrations. In future research, we wanted to optimize  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Traditional Method** & \(\rightarrow\) & \multicolumn{2}{c}{**RGRA-AEM**} \\ \cline{2-5}
**Statistical Indicators** & **No Additional** & & & \\
**Interpolation Points** & **Only Increase** & **Only Increase in** & **Increase Penetration Valve** \\
**Through the Valve** & **Penetration Valve** & **Bias Tolerance** & & **Interpolation Points** \\ \cline{2-5}  & **No Increase in** & **Interpolation Points** & **by 5\%** & **Increase in Bias Tolerance** \\  & **Bias Tolerance** & & & **by 5\%** \\ \hline Total number of positions & 472 & 589 & 472 & 598 \\ Number of data & 0 & 126 & 200 & 326 \\ points corrected & 80.4016 & 81.8313 & 81.3763 & 83.0590 \\ Accuracy/(\%) & 95.0768 & 95.6098 & 95.4465 & 96.0231 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of evaluation results between two grey relational analysis methods.  
these methods from a more data-driven perspective. It is an important to select a more reasonable hyperparameter (e.g., the acceptable degree of bias p) that is less disturbed by human factors.  
Conceptualization, J.F. and J.L.; methodology, K.Z.; validation, K.Z. and H.S.; formal analysis, K.Z.; investigation, K.Z. and R.X.; resources, J.F. and J.L.; data curation, K.Z.; writing--original draft preparation, K.Z. and Y.X.; writing--review and editing, J.F.; visualization, R.L.; supervision, J.F.; project administration, G.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Thanks to [PERSON] for his generous support of this article.  
Author [PERSON] was employed by the company China Academy of Launch Vehicle Technology. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.