Qiu, C, Su, C, Liu, X and Yu, D (2022) A Study of Feature Construction Based on Least Squares and RBF Neural Networks in Sports Training Behaviour Prediction. Computational Intelligence and Neuroscience, 2022. ISSN 1687-5265

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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.

Item Type:	Article
Uncontrolled Keywords:	1109 Neurosciences, 1702 Cognitive Sciences
Subjects:	T Technology > T Technology (General) G Geography. Anthropology. Recreation > GV Recreation Leisure > GV561 Sports
Divisions:	Engineering
Publisher:	Hindawi
Related URLs:	Author
Date Deposited:	04 Apr 2022 10:01
Last Modified:	04 Apr 2022 10:01
DOI or ID number:	10.1155/2022/5034081
URI:	https://researchonline.ljmu.ac.uk/id/eprint/16585

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