Beijing Sport University, Haidian, Beijing 100084, China.
Beijing Foreign Studies University, Haidian, Beijing 100089, China.
Comput Intell Neurosci. 2022 Apr 22;2022:2078642. doi: 10.1155/2022/2078642. eCollection 2022.
It has been widely used in signal processing, image processing, speech recognition and synthesis, pattern recognition, machine vision, machinery fault diagnosis and monitoring, and other scientific and technological fields and has achieved great results. The application potential in nonlinear system identification is increasing. According to the theory of "overload recovery" and "functional reserve", the mathematical model of "load-fitness state" is established to understand the adaptation characteristics and individual characteristics of athletes to sports training. The model is used to simulate the values and time required to reach the maximum fitness state for four types of precompetition reduction plans and to provide a reference for the development of precompetition training plans. The data required for parameter estimation were the actual training data of six outstanding basketball athletes (mean age 18.2 ± 0.75, mean training years 4.6 ± 0.49). And the coaches' training plan was not intervened during the test. In order to further reduce the biaxial synchronization error of the sports platform and improve the stability of the system, the wavelet transformation capable of time-varying signal analysis and the recursive structure with dynamic capability were combined with the fuzzy neural network, and the learning ability of the neural network was used to learn and adjust the scaling and translation factors in the wavelet function, the mean and standard deviation in the fuzzy structure, and the connection weights between the layers, according to the biaxial synchronization. The simulation results show that the designed global sliding mode controller can improve the convergence speed of tracking error and ensure the single-axis tracking accuracy of the H-type motion platform compared with the traditional sliding mode controller, and the tracking accuracy and synchronization accuracy of the system can be further improved after adding the cross-coupled synchronization controller, but the improvement of synchronization control accuracy is not very satisfactory due to the fixed selection of the parameters of the cross-coupled controller. Further improvement is needed.
它已广泛应用于信号处理、图像处理、语音识别与合成、模式识别、机器视觉、机械故障诊断与监测等科学技术领域，并取得了巨大的成果。在非线性系统辨识中的应用潜力不断增加。根据“过载恢复”和“功能储备”理论，建立了“负荷-适应状态”的数学模型，以了解运动员对运动训练的适应特征和个体特征。该模型用于模拟四种赛前减脂计划达到最大适应状态所需的数值和时间，并为赛前训练计划的制定提供参考。参数估计所需的数据是六位优秀篮球运动员的实际训练数据（平均年龄 18.2±0.75，平均训练年限 4.6±0.49）。并且在测试过程中教练的训练计划没有被干预。为了进一步降低运动平台的双轴同步误差，提高系统的稳定性，将具有时变信号分析能力的小波变换与具有动态能力的递归结构相结合，利用神经网络的学习能力，学习和调整小波函数中的缩放和平移因子、模糊结构中的均值和标准差以及层间的连接权重，根据双轴同步。仿真结果表明，与传统滑模控制器相比，设计的全局滑模控制器可以提高跟踪误差的收敛速度，保证 H 型运动平台的单轴跟踪精度，并且在添加交叉耦合同步控制器后可以进一步提高系统的跟踪精度和同步精度，但是由于交叉耦合控制器参数的固定选择，同步控制精度的提高并不十分理想。需要进一步改进。
