Abstract
In order to improve the accuracy of shooting in basketball. A shooting accuracy prediction method based on the convergent improved resource allocating network (CIRAN) online radial basis function neural network (RBFNN) is proposed, and the RBFNN learning algorithm is improved. Through the collection of shooting motion images, feature point extraction, and edge contour feature extraction, the shooting motion trajectory is obtained. Using the online neural network based on the CIRAN learning algorithm to predict the accuracy of shooting, this method analyzes the radial basis function (RBF) network. Based on the RBF analysis, the number of network layers and the number of hidden layer neurons are adjusted and optimized. In order to improve the prediction accuracy of shooting in basketball, a method based on. Through the analysis, it can be known that the accuracy of both the traditional RBFNN and the CIRAN-based online neural network for the prediction of shooting accuracy is above 70%. The prediction accuracy of the online neural network for shooting is higher than that of the traditional one. This is mainly because the online update function of the learning algorithm can better adjust the corresponding structure with the development of the game and has a better generalization ability. In addition, because the CIRAN learning algorithm introduces the hidden layer neuron deletion strategy, its network structure is simpler than that of the traditional one, the number of hidden layer neurons is less, and the running time required is less, which can better meet the real-time requirements and provide a more scientific method for basketball training.
Highlights
With the development of computer image processing technology, embedded digital image and video information analysis methods are used to carry out image analysis and feature extraction of sports, establish a feature analysis model of sports images, and improve the ability of feature identification and movement correction of sports
E main problems of the aforementioned MRAN learning algorithm are as follows: firstly, due to the use of the extended Kalman filter to adjust the network parameters, the parameters must be updated in each iteration, which leads to the process of updating the parameters with the hidden layer neurons. e scale of the matrix is very large, which increases the computational complexity of the radial basis function neural network (RBFNN) structure, causes the algorithm to calculate too much burden, consumes a lot of computer resources, and limits the real-time application of the MRAN algorithm; initializing the algorithm, there are too many parameters, and improper selection of initialization parameters will greatly reduce the performance of the algorithm
This paper proposes a convergent improved resource allocating network (CIRAN) improved algorithm, which is mainly reflected in the following: (i) In order to reduce the initialization parameters of the algorithm, the idea of the GAP-RBP algorithm is absorbed, only the parameters of the hidden layer neuron closest to the current input data are updated, and the definition and estimation formula for measuring the importance of the hidden layer neuron are introduced, which reduces the algorithm number of initialization parameters and improves the generalization performance of the algorithm to a certain extent. e importance of hidden layer neurons is defined as follows: Eimp(k)
Summary
With the development of computer image processing technology, embedded digital image and video information analysis methods are used to carry out image analysis and feature extraction of sports, establish a feature analysis model of sports images, and improve the ability of feature identification and movement correction of sports. Filtering for dynamic tracking, fusion with chaos theory to reconstruct the phase space of the athlete’s motion trajectory, the chaotic invariant representing the athlete’s motion trajectory is extracted from the reconstructed phase space, the motion trajectory with three-dimensional space characteristics is converted into a one-dimensional motion trajectory, and the optimized recognition of the volleyball player’s trajectory is completed. This method has a low accuracy in predicting the trajectory of volleyball players.
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