Analysis and Prediction of Body Test Results Based on Improved Backpropagation Neural Network Algorithm

Table 1

Research progress and summary of key work.

Researcher	Related work	Features	Innovative points
Zhong Wu, Tang Yuenian	A predictive model established for the correlation between specific achievements and quality training indicators.	More accurately maps the functional relationship between athlete quality training indicators and special sports performance.	This model overcomes the disadvantages of multiple regression models and grey models that require mathematical models to be determined in advance.
Zhong Ping, Gong Mingbo	The historical data of the group corresponding to the 4 quality training indicators and the special results are used as the training samples of the neural network.	Accurately maps the functional relationship between the athlete’s quality training level and the special achievement.	Using artificial neural networks, a neural network model reflecting the correlation between physical fitness and special achievements is proposed.
Zhang Jingzhi	A third-order grey neural network model based on the time response function is established.	Improves the accuracy of grey neural network models simulating complex nonlinear dynamic processes.	A time-based response function is established.
Wang Yue	The attribute value of the underlying indicator acts as the input vector.	The weight factor value is that the network is correctly represented internally through adaptive learning.	The training can be used as an effective tool for combining qualitative and quantitative data.