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Planning Training Loads to Develop Technique and Rhythm in the 400m Hurdles using RBF Network

In this paper training loads to develop technique and rhythm in hurdles are presented. The training loads were generated using an artificial neural networks model with radial basis functions. The analysis included 21 hurdlers who were members of the Polish National Team. The calculations for the neural model were made using 48 training programmes. The evaluation of the models was carried out using the cross-validation method. Five independent variables (age, body height, body weight, current result and expected result) and four dependent variables representing the selected training loads were analyzed. The determined model generated training loads with an error of approximately 21%. Experimental results showed the training programme for a hypothetical athlete. The analysis shows that all the examined training loads are of a non-linear nature. The proposed solution can be used as a tool to support planning for selected training loads in 400 m hurdles.