The advanced analysis and research methods of big data will provide theoretical support for the integration of athletes’ talent training. The advanced technological methods of big data will also give full play to the advantages of tapping the potential of talents and actively improve the success rate of grassroots young athletes. This paper proposes an improved Adaptive Particle Swarm Optimization (APSO) algorithm for the optimization of radial basis function (RBF) neural network parameters. The basic structure of RBF neural network is introduced, and the influence of parameters on the performance of RBF neural network is analyzed. The optimization method of RBF neural network parameters is analyzed, and Particle Swarm Optimization (PSO) algorithm is selected as the parameter optimization method of RBF neural network. In addition, an improved APSO algorithm is proposed according to the advantages and disadvantages of PSO and compared with other PSO algorithms. Experimental results show that the improved PSO algorithm has better accuracy. The improved PSO algorithm is applied to the parameter optimization of RBF neural network, and the experimental results prove the superiority of the proposed method. By weighting the second-level indicators, the weights of the second-level indicators of athletes’ competitive ability are in order of skill, athletic quality, psychological ability, and artistic expression. Skills are the main factors that determine the level of competitive ability. Sports quality and psychological ability are important guarantees for supporting the normal performance of skills. Artistic expressiveness is a supplementary factor for competitive ability. The various elements cooperate with each other and interact with each other. The indicators do not exist alone but cooperate with each other to support the formation of the entire athletic ability system. In the content of the competitive ability index of excellent athletes, technical ability is the core, and sports quality, psychological ability, and artistic performance ability ultimately exist to serve the improvement of technical ability. The competition scores of the 100 athletes counted in this article are all above 9.0 points. The difference between APSO-RBF’s action quality scores of 100 athletes and the real value is less than 3%. In terms of movement difficulty, the APSO-RBF evaluated athletes’ scores are close to 1.65 points, which is basically the same as the real value.

中文翻译：

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基于APSO-RBF神经网络的信号识别辅助运动员竞技能力评估

大数据先进的分析和研究方法将为运动员人才培养一体化提供理论支撑。大数据的先进技术手段也将充分发挥挖掘人才潜力的优势，积极提高基层青少年运动员的成功率。本文提出了一种改进的自适应粒子群优化（APSO）算法，用于优化径向基函数（RBF）神经网络参数。介绍了RBF神经网络的基本结构，分析了参数对RBF神经网络性能的影响。分析了RBF神经网络参数的优化方法，选择粒子群优化(PSO)算法作为RBF神经网络的参数优化方法。此外，根据PSO的优缺点并与其他PSO算法进行比较，提出了一种改进的APSO算法。实验结果表明，改进的PSO算法具有更好的精度。将改进的PSO算法应用于RBF神经网络的参数优化中，实验结果证明了该方法的优越性。通过对二级指标进行加权，运动员竞技能力二级指标的权重依次为技术、运动素质、心理能力、艺术表现力。技能是决定竞技能力水平的主要因素。运动素质和心理能力是支持技能正常发挥的重要保证。艺术表现力是竞技能力的补充因素。各个要素相互配合、相互作用。这些指标并不是单独存在的，而是相互配合支撑整个运动能力体系的形成。优秀运动员竞技能力指标内容中，技术能力是核心，运动素质、心理能力、艺术表现能力最终都是为了服务于技术能力的提高而存在的。本文统计的100名运动员的比赛成绩均在9.0分以上。APSO-RBF对100名运动员的动作质量评分与真实值的差异小于3%。在动作难度方面，APSO-RBF评估运动员的得分接近1.65分，与真实值基本一致。