Literature DB >> 17282870

Artificial neural networks as a tool of modeling of training loads.

Igor Rygula1.   

Abstract

This paper shows that extremely important element of forming speed capabilities is proper (quantitative) structure of exercise loads. This means that training means should be chosen from point of view of energy production in metabolic processes, which depends on the structure of training means from the information area and energy area, therefore on the character of work made, its intensity, duration of exercise, number of repetitions and duration of rest periods. From the training process effectiveness point of view, it is extremely important to find the correct tool for choosing means in given training cycle. The investigation results confirm the experiences of coaches and theorists of sport, that the structure of volume and intensity of exercise loads should be individually chosen with consideration of predispositions of separate athletes. Individualization of training is condition for its optimization.

Year:  2005        PMID: 17282870     DOI: 10.1109/IEMBS.2005.1617101

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  3 in total

1.  Regression shrinkage and neural models in predicting the results of 400-metres hurdles races.

Authors:  K Przednowek; J Iskra; A Maszczyk; M Nawrocka
Journal:  Biol Sport       Date:  2016-11-10       Impact factor: 2.806

2.  Predictive Modeling in Race Walking.

Authors:  Krzysztof Wiktorowicz; Krzysztof Przednowek; Lesław Lassota; Tomasz Krzeszowski
Journal:  Comput Intell Neurosci       Date:  2015-08-03

3.  Planning Training Loads for the 400 M Hurdles in Three-Month Mesocycles using Artificial Neural Networks.

Authors:  Krzysztof Przednowek; Janusz Iskra; Krzysztof Wiktorowicz; Tomasz Krzeszowski; Adam Maszczyk
Journal:  J Hum Kinet       Date:  2017-12-28       Impact factor: 2.193

  3 in total

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