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Real-Time Regulation of Physical Training Intensity Based on Fuzzy Neural Network
Journal of Circuits, Systems and Computers ( IF 0.9 ) Pub Date : 2022-09-09 , DOI: 10.1142/s0218126623500445
Jiale Qu 1
Affiliation  

In this paper, the fuzzy neural network model is studied, the real-time regulation model of physical training intensity is analyzed and a real-time regulation system based on a fuzzy neural network is designed. The real-time, accurate and effective regulation of the physiological load intensity in the body of the exerciser is consistent with the predetermined goals of the training program. In this paper, we propose an RBF neural network, combined with the plan and demand of physical training operation situation sensing, and considering that most of the biological training operation data is fuzzy, this paper connects a fuzzy logic inference system and a neural network and proposes a network operation situation sensing model based on an RBF neural network structure. The RBF neural network and the traditional fuzzy neural network are compared. The experiments prove that this paper’s fuzzy neural network model has a faster training speed. In this paper, we use time-realistic control equipment to monitor the physical training process of athletes so that we can grasp the training situation of athletes in real-time and ensure that athletes can achieve better training results by changing training methods and changing training loads in time for those athletes who cannot reach their sports goals. In the process of physical fitness training monitoring, an effective monitoring of training, time-accurate regulation monitoring has the advantage of timely feedback on the training situation. This model has a better convergence effect during exercise and a higher accuracy of posture prediction during testing.



中文翻译:

基于模糊神经网络的体育训练强度实时调控

本文对模糊神经网络模型进行了研究,分析了体育训练强度的实时调控模型,设计了一个基于模糊神经网络的实时调控系统。实时、准确、有效地调节锻炼者体内的生理负荷强度,与预定的训练计划目标相一致。在本文中,我们提出了一种RBF神经网络,结合体育训练操作态势感知的计划和需求,并考虑到大多数生物训练操作数据是模糊的,本文连接了一个模糊逻辑推理系统和一个神经网络和提出了一种基于RBF神经网络结构的网络运行态势感知模型。RBF神经网络与传统的模糊神经网络进行了比较。实验证明本文的模糊神经网络模型具有较快的训练速度。本文通过实时控制设备对运动员的体能训练过程进行监控,实时掌握运动员的训练情况,确保运动员通过改变训练方式、改变训练负荷来达到更好的训练效果。及时为那些无法达到运动目标的运动员。在体能训练监测过程中,对训练进行有效的监测,及时准确的调节监测,具有及时反馈训练情况的优点。该模型在运动过程中具有更好的收敛效果,在测试过程中具有更高的姿势预测准确率。本文通过实时控制设备对运动员的体能训练过程进行监控,实时掌握运动员的训练情况,确保运动员通过改变训练方式、改变训练负荷来达到更好的训练效果。及时为那些无法达到运动目标的运动员。在体能训练监测过程中,对训练进行有效的监测,及时准确的调节监测,具有及时反馈训练情况的优点。该模型在运动过程中具有更好的收敛效果,在测试过程中具有更高的姿势预测准确率。本文通过实时控制设备对运动员的体能训练过程进行监控,实时掌握运动员的训练情况,确保运动员通过改变训练方式、改变训练负荷来达到更好的训练效果。及时为那些无法达到运动目标的运动员。在体能训练监测过程中,对训练进行有效的监测,及时准确的调节监测,具有及时反馈训练情况的优点。该模型在运动过程中具有更好的收敛效果,在测试过程中具有更高的姿势预测准确率。我们采用时间真实的控制设备,对运动员的体能训练过程进行监控,实时掌握运动员的训练情况,及时为运动员改变训练方式、改变训练负荷,确保运动员取得更好的训练效果。无法达到运动目标的运动员。在体能训练监测过程中,对训练进行有效的监测,及时准确的调节监测,具有及时反馈训练情况的优点。该模型在运动过程中具有更好的收敛效果,在测试过程中具有更高的姿势预测准确率。我们采用时间真实的控制设备,对运动员的体能训练过程进行监控,实时掌握运动员的训练情况,及时为运动员改变训练方式、改变训练负荷,确保运动员取得更好的训练效果。无法达到运动目标的运动员。在体能训练监测过程中,对训练进行有效的监测,及时准确的调节监测,具有及时反馈训练情况的优点。该模型在运动过程中具有更好的收敛效果,在测试过程中具有更高的姿势预测准确率。在体能训练监测过程中,对训练进行有效的监测,及时准确的调节监测,具有及时反馈训练情况的优点。该模型在运动过程中具有更好的收敛效果,在测试过程中具有更高的姿势预测准确率。在体能训练监测过程中,对训练进行有效的监测,及时准确的调节监测,具有及时反馈训练情况的优点。该模型在运动过程中具有更好的收敛效果,在测试过程中具有更高的姿势预测准确率。

更新日期:2022-09-09
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