Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Mar 7:2022:5034081.
doi: 10.1155/2022/5034081. eCollection 2022.

A Study of Feature Construction Based on Least Squares and RBF Neural Networks in Sports Training Behaviour Prediction

Chunyan Qiu  1 , Changhong Su  2 , Xiaoxiao Liu  3 , Dian Yu  1
Affiliations

A Study of Feature Construction Based on Least Squares and RBF Neural Networks in Sports Training Behaviour Prediction

Chunyan Qiu et al. Comput Intell Neurosci. .

Retraction in

Abstract

This paper examines the problem of athletes' training in sports, exploring the methods and means by which athletes can perform difficult movements in which they normally make minor training errors in order to achieve better competition results and placements. To this end, we test the explanatory and predictive effects of a theoretical model starting with planned behaviour and then use exercise planning, self-efficacy, and support as variables to develop a partial least squares regression model of sports to improve the explanation and prediction of sporting athletes' intentions and behaviour. An improved RBF network-based method for player behaviour prediction is proposed. On the basis of the RBF analysis, the number of layers and the number of neurons in the hidden layer of the network are adjusted and optimised, respectively, to improve its generalisation and learning abilities, and the athlete behaviour prediction model is given. The results demonstrate the advantages of the improved algorithm, which in turn provides a more scientific approach to the current basketball training.

PubMed Disclaimer

Conflict of interest statement

The authors declare that there are no conflicts of interest.

Figures

Figure 1
Figure 1
Topology of a single-output RBF network.
Figure 2
Figure 2
Typical training sample of the head state detection classifier.
Figure 3
Figure 3
Accuracy of head pattern recognition versus relative size of small areas.
Figure 4
Figure 4
Comparison of recognition rates for different types of original feature fusion.
Figure 5
Figure 5
Average head state recognition in different occlusion situations.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Feng Q. Study on recognition of cross and intermediate features based on RBF Neural Networks. WSEAS Transactions on Computers . 2006;5(9):1831–1836.
    1. Han H.-G., Qiao J.-F. Prediction of activated sludge bulking based on a self-organizing RBF neural network. Journal of Process Control . 2012;22(6):1103–1112. doi: 10.1016/j.jprocont.2012.04.002. - DOI
    1. Melagraki G., Afantitis A., Makridima K., Sarimveis H., Igglessi-Markopoulou O. Prediction of toxicity using a novel RBF neural network training methodology. Journal of Molecular Modeling . 2006;12(3):297–305. doi: 10.1007/s00894-005-0032-8. - DOI - PubMed
    1. Addeh A., Khormali A., Golilarz N. A. Control chart pattern recognition using RBF neural network with new training algorithm and practical features. ISA Transactions . 2018;79:p. S0019057818301666. doi: 10.1016/j.isatra.2018.04.020. - DOI - PubMed
    1. Huaien Luo H., Puthusserypady S. Estimation of the hemodynamic response of fMRI data using RBF neural network. IEEE Transactions on Biomedical Engineering . 2007;54(8):1371–1381. doi: 10.1109/tbme.2007.900795. - DOI - PubMed

Publication types

LinkOut - more resources