TY -的A2 -艾哈迈德,赛义德·哈桑AU -郭,冯盟——黄,青城山PY - 2021 DA - 2021/07/23 TI -信号识别基于APSO-RBF神经网络来帮助运动员的竞争力评价SP - 4850020六世- 2021 AB -大数据的高级分析和研究方法将提供理论支持为一体的运动员人才培训。大数据的先进的技术方法也将充分发挥的优势利用的潜力人才,积极改善基层年轻运动员的成功率。本文提出了一种改进的自适应粒子群优化(阿普索犬)算法优化的径向基函数(RBF)神经网络参数。介绍了RBF神经网络的基本结构和参数对RBF神经网络的性能的影响进行了分析。RBF神经网络参数的优化方法,分析了选择和粒子群优化(PSO)算法的RBF神经网络的参数优化方法。此外,一种改进的皮犬算法根据算法的优缺点和相比其他PSO算法。实验结果表明,改进的PSO算法具有更好的精度。改进的PSO算法应用于RBF神经网络的参数优化和实验结果证明该方法的优越性。通过加权二级指标,二级指标的权重运动员竞技能力的技能,运动质量、心理能力、和艺术表达。技能是决定竞技能力水平的主要因素。 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. SN - 1687-5265 UR - https://doi.org/10.1155/2021/4850020 DO - 10.1155/2021/4850020 JF - Computational Intelligence and Neuroscience PB - Hindawi KW - ER -