THE USE OF ARTIFICIAL NEURAL NETWORKS IN SUPPORTING THE ANNUAL TRAINING IN 400 METER HURDLES
Journal Title: Central European Journal of Sport Sciences and Medicine - Year 2017, Vol 17, Issue 1
Abstract
This paper presents an evaluation of the annual cycle for 400 m hurdles using artificial neural networks. The analysis included 21 Polish national team hurdlers. In planning the annual cycle, 27 variables were used, where 5 variables describe the competitor and 22 variables represent the training loads. In the presented solution, the task of generating training loads for the assumed result were considered. The neural models were evaluated by cross-validation method. The smallest error was obtained for the radial basis function network with nine neurons in the hidden layer. The performed analysis shows that at each phase of training the structure of training loads is different.
Authors and Affiliations
Janusz Iskra, Krzysztof Przednowek, Tomasz Krzeszowski
Keywords
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- EP ID EP215131
- DOI 10.18276/cej.2017.1-02
- Views 93
- Downloads 0
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