Abstract des Autors

The purpose of the paper is to demonstrate that the performance of an elite female swimmer in the finals of the 200-m backstroke at the Olympic Games 2000 in Sydney can be predicted by means of the nonlinear mathematical method of artificial neural networks (Multi-Layer Perceptrons). The data con-sisted of the performance output of 19 competitions (200-m backstroke) prior to the Olympics and the training input data of the last 4 weeks prior to each competition. Multi-Layer Perceptrons with 10 input neurons, 2 hidden neuron, and 1 output neuron were used. Since the data of 19 competitions are insuffi-cient to train such networks, the training input and competition data of another athlete were used in the training processes of the neural networks to pre-train the neural networks. The neural models were validated by the “leave-one-out”; method, then the neural models were used to predict the Olympic competitive performance. The results show that the modeling was very precise; the error of the prediction was only 0.05 s, with a total swim time of 2:12.64 min:s. Verf.-Referat