The structure of the deep artificial neural network is similar to the structure of the biological neural network, which can be well applied to the 3D visual image recognition of aerobics movements. A lot of results have been achieved by applying deep neural networks to the 3D visual image recognition of aerobics movements, but there are still many problems to be overcome. After analyzing the expression characteristics of the convolutional neural network model for the three-dimensional visual image characteristics of aerobics, this paper builds a convolutional neural network model. The model is improved on the basis of the traditional model and unifies the process of aerobics 3D visual image segmentation, target feature extraction, and target recognition. The convolutional neural network and the deep neural network based on autoencoder are designed and applied to aerobics action 3D visual image test set for recognition and comparison. We improve the accuracy of network recognition by adjusting the configuration parameters in the network model. The experimental results show that compared with other simple models, the model based on the improved AdaBoost algorithm can improve the final result significantly when the accuracy of each model is average. Therefore, the method can improve the recognition accuracy when multiple neural network models with general accuracy are obtained, thereby avoiding the complicated parameter adjustment process to obtain a single optimal network model.
Aiming at some problems existing in the existing sports monitoring system, based on the joint action of OneNet Internet of Things (IoT) and cloud platform, an optimized adaptive fuzzy PID control algorithm is adopted to monitor and analyze sports. Finally, the accuracy of the optimization model is verified through the comparison of different models, and the algorithm is used to predict and analyze sports. The research shows that (1) as the index calculation of cloud platform shows, with the increase of iteration time, the change curve of relevant indexes can be divided into four different stages, namely, rapid fluctuation stage, slow decline stage, slow fluctuation stage, and rapid decline stage. (2) The conventional calculation method (CPID) cannot well describe the change rule of the test data in the early stage of settlement. The fuzzy adaptive calculation method (NPID) also exposes some errors in the fitting and description of the test curve in the calculation process, while the improved adaptive calculation method (GPID) can describe the change characteristics and rules of the test curve well for different stages. (3) Compared with the original model, the optimization model can better describe the first and second stages of index change, indicating the accuracy of the optimization model. And the algorithm can be used to predict and analyze the changes of indicators and sports monitoring better, and the analysis results can provide relevant guidance for sports monitoring. This optimization scheme provides basis and theoretical support for the application of OneNet IoT and cloud platform.
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