A NONLINEAR APPROACH TO THE ANALYSIS AND MODELING OF TRAINING AND ADAPTATION IN SWIMMING

Abstract

The purpose of the study was to demonstrate that the adaptative behavior of an elite female swimmer (Olympic silver medalist in the 400 m freestyle) can be modeled by means of the nonlinear mathematical method of a neural backpropagation network. Therefore, the training process of 107 successive weeks was carefully controlled and documented. For the data analysis a multilayer perceptron network was trained with the performance output data of 28 competitions within that time period and the training input data of the last four weeks prior to the respective competitions. After the iterative training procedure the neural network is able to model the resulting competitive performances on the basis of the training data from the two-week-taper phase and also from the earlier two-week-overload phase preceeding the respective competitions with high precision.