Wu Qiong, Sun Yi, Gao Lei, Yin Wanxing
College of physical education, China Three Gorges University, Yichang, Hubei, China.
Graduate School, Philippine Christian University, Malate, Manila, Philippines.
PLoS One. 2025 Jun 2;20(6):e0316200. doi: 10.1371/journal.pone.0316200. eCollection 2025.
To improve the competitive state of badminton athletes and summarize the technical characteristics of badminton players, this paper introduces multi-dimensional fuzzy removal intelligent computing. Taking 120 badminton students from a sports school as data samples, the sports images of athletes are collected, the images are enhanced using histogram equalization, and then the fuzzy clustering algorithm is used to analyze the characteristics of the pictures. The following results were obtained from the analysis of the understanding degree of motion decomposition, the analysis of the lasting effect, the study of the number of repetitions, and the analysis of the simulation results: The degree of understanding was 17.75% higher than that of traditional training methods; the effect was better than that of conventional training methods; the traditional training method had a small number of action repetitions; the performance of boys and girls in the temporary mock exam would be related to different training methods. Therefore, this paper had practical significance for this research, to help promote such academic and give reference. At the same time, most optimization problems needed to comprehensively consider many factors, so multi-objective optimization algorithms became a hot spot in academic research.
为提高羽毛球运动员的竞技状态，总结羽毛球运动员的技术特征，本文引入了多维模糊去除智能计算。以某体校120名羽毛球专业学生作为数据样本，采集运动员的运动图像，采用直方图均衡化对图像进行增强，然后运用模糊聚类算法分析图片特征。通过对动作分解理解程度分析、持续效果分析、重复次数研究以及模拟结果分析，得到以下结果：理解程度比传统训练方法高17.75%；效果优于传统训练方法；传统训练方法的动作重复次数较少；男女生在临时模拟考试中的表现会与不同训练方法有关。因此，本文的研究具有实际意义，有助于推动此类学术研究并提供参考。同时，大多数优化问题需要综合考虑诸多因素，所以多目标优化算法成为学术研究的热点。
