NEURAL NETWORKS FOR FORECASTING AND MODELING TRAINING IN TRACK-AND-FIELD ATHLETICS
Keywords:
Neural networks, training process, physical fitness
Abstract
Aim. The article deals with the application of neural networks for forecasting the most optimal ways and intensity of training for 400-meter runners at the stage of performance improvement. Materials and methods. 400-meter male runners aged 18–21 participated in the study (8 runners of the first rank, 3 runners with the rank of Candidate for Master of Sport). During the study, we used Neural Network v2.4.2 software developed by Jwsoft.Net. Initial data consisted of 8 indicators for each athlete (n = 10) taken in compliance with the months of a one-year training cycle 2014/2015, 2015/2016, 2016/2017. Network training was performed with the algorithm of the error back propagation. Results. To simulate physical preparedness of 400-meter runners in 2016/2017, we inserted into a trained neural network the parameters of monthly volume load, which allowed us to forecast competition results for the 400-meter runners of the first rank and of the rank of Candidate for Master of Sport. The reliability of forecasting is 98–99 %. The method proposed based on the application of the neural network allows to quickly estimate the dynamics of physical preparedness. This provides the reliability and quality of forecasting based on the training plan. Conclusion. The application of neural networks will allow to determine the most optimal ways and volumes of training. The coach will have a tool, which allows him to make effective decisions about the correction of training.References
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2. Krivetskiy I.Yu., Popov G.I., Bezrukov N.S. [Creating an Individual Model of the High Jump Technique Based on a Cascade Neuro-Fuzzy Network in Order to Optimize the Training Process]. Rossiyskiy zhurnal biomekhaniki [Russian Journal of Biomechanics], 2011, vol. 15, no. 3 (53), pp. 71–78. (in Russ.)
3. Zelichenoka V.B. et al. Metodicheskiye rekomendatsii po sovershenstvovaniyu mnogoletney podgotovki sportivnogo rezerva v legkoy atletike [Guidelines for Improving the Long-Term Training of Sports Reserve in Athletics]. Moscow, Center for the development of athletics IAAF Publ., 2017. 543 p. Available at: http://la.sportedu.ru/content/metodicheskie-rekomendatsii-poovershenstvovaniyumnogoletnei-podgotovki-sportivnogo-reze-0-25.03.2018 (accessed 25.03.2018).
4. Neural network v2.4.2. Available at: http://kazus.ru/programs/viewdownloaddetails/ kz_0/lid_13563.html.
5. Parfianovich E.V., Bobkova E.N. [Pedagogical Experience of Applying Statistical Methods and Modeling in the Field of Physical Education]. Aktual’nyye problemy teorii i praktiki fizicheskoy kul’tury, sporta i turizma: materialy V Vserossiyskoy nauchno-prakticheskoy konferentsii molodykh uchenykh, aspirantov, magistrantov i studentov [Actual Problems of the Theory and Practice of Physical Culture, Sport and Tourism. Materials of the V All-Russian Scientific and Practical Conference of Young Scientists, Graduate Students, Undergraduates and Students], 2017, vol. 3, pp. 673–676. (in Russ.)
6. Semenova A.A. Primeneniye neyronnykh setey dlya prognozirovaniya rezul’tatov v sporte [The Use of Neural Networks to Predict Results in Sports]. Available at: http://uran.donntu.org/-masters/2006/kita/kornev/library/l15.htm (accessed 15.05.2018).
7. Field A. Discovering Statistics using SPSS. Washington DC, Sage Publications, 2009, pp. 585–626.
8. Lee G., Bucheli D.E.R., Madabhushi A. Adaptive Dimensionality Reduction with Semi-Supervision (AdReSS): Classifying Multi-Attribute Biomedical Data. PLoS ONE, 2014, vol. 11, pp. 14–16.
9. Hornik K., Stinchcombe М., White H. Multilayer Feed Forward Networks are Universal Approximators. Artificial Neural Networks. Approximation and Learning Theory. Blackwell, Oxford, UK, 2012, pp. 12–28.
10. Leung K-S., Lee K.H., Wang J-F. et al. Data Mining on DNA Sequences of Hepatitis B Virus. Transactions on Computing Biology and Bioinformatics, 2011, vol. 8, no. 2, pp. 428-440. DOI: 10.1109/TCBB.2009.6
11. Leech N.L., Barett K.C., Morgan G.A. IBM SPSS for Intermediate Statistics, 5th ed. New York and London: Routledge, Taylor & Francis Group, 2015, pp. 109–143.
2. Krivetskiy I.Yu., Popov G.I., Bezrukov N.S. [Creating an Individual Model of the High Jump Technique Based on a Cascade Neuro-Fuzzy Network in Order to Optimize the Training Process]. Rossiyskiy zhurnal biomekhaniki [Russian Journal of Biomechanics], 2011, vol. 15, no. 3 (53), pp. 71–78. (in Russ.)
3. Zelichenoka V.B. et al. Metodicheskiye rekomendatsii po sovershenstvovaniyu mnogoletney podgotovki sportivnogo rezerva v legkoy atletike [Guidelines for Improving the Long-Term Training of Sports Reserve in Athletics]. Moscow, Center for the development of athletics IAAF Publ., 2017. 543 p. Available at: http://la.sportedu.ru/content/metodicheskie-rekomendatsii-poovershenstvovaniyumnogoletnei-podgotovki-sportivnogo-reze-0-25.03.2018 (accessed 25.03.2018).
4. Neural network v2.4.2. Available at: http://kazus.ru/programs/viewdownloaddetails/ kz_0/lid_13563.html.
5. Parfianovich E.V., Bobkova E.N. [Pedagogical Experience of Applying Statistical Methods and Modeling in the Field of Physical Education]. Aktual’nyye problemy teorii i praktiki fizicheskoy kul’tury, sporta i turizma: materialy V Vserossiyskoy nauchno-prakticheskoy konferentsii molodykh uchenykh, aspirantov, magistrantov i studentov [Actual Problems of the Theory and Practice of Physical Culture, Sport and Tourism. Materials of the V All-Russian Scientific and Practical Conference of Young Scientists, Graduate Students, Undergraduates and Students], 2017, vol. 3, pp. 673–676. (in Russ.)
6. Semenova A.A. Primeneniye neyronnykh setey dlya prognozirovaniya rezul’tatov v sporte [The Use of Neural Networks to Predict Results in Sports]. Available at: http://uran.donntu.org/-masters/2006/kita/kornev/library/l15.htm (accessed 15.05.2018).
7. Field A. Discovering Statistics using SPSS. Washington DC, Sage Publications, 2009, pp. 585–626.
8. Lee G., Bucheli D.E.R., Madabhushi A. Adaptive Dimensionality Reduction with Semi-Supervision (AdReSS): Classifying Multi-Attribute Biomedical Data. PLoS ONE, 2014, vol. 11, pp. 14–16.
9. Hornik K., Stinchcombe М., White H. Multilayer Feed Forward Networks are Universal Approximators. Artificial Neural Networks. Approximation and Learning Theory. Blackwell, Oxford, UK, 2012, pp. 12–28.
10. Leung K-S., Lee K.H., Wang J-F. et al. Data Mining on DNA Sequences of Hepatitis B Virus. Transactions on Computing Biology and Bioinformatics, 2011, vol. 8, no. 2, pp. 428-440. DOI: 10.1109/TCBB.2009.6
11. Leech N.L., Barett K.C., Morgan G.A. IBM SPSS for Intermediate Statistics, 5th ed. New York and London: Routledge, Taylor & Francis Group, 2015, pp. 109–143.
References on translit
1. Bondarchuk A.P. Upravleniye trenirovochnym protsessom sportsmenov vysokogo klassa [Management of the Training Process of High-Class Athletes]. Moscow, Olympia Press Publ., 2007. 272 p.2. Krivetskiy I.Yu., Popov G.I., Bezrukov N.S. [Creating an Individual Model of the High Jump Technique Based on a Cascade Neuro-Fuzzy Network in Order to Optimize the Training Process]. Rossiyskiy zhurnal biomekhaniki [Russian Journal of Biomechanics], 2011, vol. 15, no. 3 (53), pp. 71–78. (in Russ.)
3. Zelichenoka V.B. et al. Metodicheskiye rekomendatsii po sovershenstvovaniyu mnogoletney podgotovki sportivnogo rezerva v legkoy atletike [Guidelines for Improving the Long-Term Training of Sports Reserve in Athletics]. Moscow, Center for the development of athletics IAAF Publ., 2017. 543 p. Available at: http://la.sportedu.ru/content/metodicheskie-rekomendatsii-poovershenstvovaniyumnogoletnei-podgotovki-sportivnogo-reze-0-25.03.2018 (accessed 25.03.2018).
4. Neural network v2.4.2. Available at: http://kazus.ru/programs/viewdownloaddetails/ kz_0/lid_13563.html.
5. Parfianovich E.V., Bobkova E.N. [Pedagogical Experience of Applying Statistical Methods and Modeling in the Field of Physical Education]. Aktual’nyye problemy teorii i praktiki fizicheskoy kul’tury, sporta i turizma: materialy V Vserossiyskoy nauchno-prakticheskoy konferentsii molodykh uchenykh, aspirantov, magistrantov i studentov [Actual Problems of the Theory and Practice of Physical Culture, Sport and Tourism. Materials of the V All-Russian Scientific and Practical Conference of Young Scientists, Graduate Students, Undergraduates and Students], 2017, vol. 3, pp. 673–676. (in Russ.)
6. Semenova A.A. Primeneniye neyronnykh setey dlya prognozirovaniya rezul’tatov v sporte [The Use of Neural Networks to Predict Results in Sports]. Available at: http://uran.donntu.org/-masters/2006/kita/kornev/library/l15.htm (accessed 15.05.2018).
7. Field A. Discovering Statistics using SPSS. Washington DC, Sage Publications, 2009, pp. 585–626.
8. Lee G., Bucheli D.E.R., Madabhushi A. Adaptive Dimensionality Reduction with Semi-Supervision (AdReSS): Classifying Multi-Attribute Biomedical Data. PLoS ONE, 2014, vol. 11, pp. 14–16.
9. Hornik K., Stinchcombe М., White H. Multilayer Feed Forward Networks are Universal Approximators. Artificial Neural Networks. Approximation and Learning Theory. Blackwell, Oxford, UK, 2012, pp. 12–28.
10. Leung K-S., Lee K.H., Wang J-F. et al. Data Mining on DNA Sequences of Hepatitis B Virus. Transactions on Computing Biology and Bioinformatics, 2011, vol. 8, no. 2, pp. 428-440. DOI: 10.1109/TCBB.2009.6
11. Leech N.L., Barett K.C., Morgan G.A. IBM SPSS for Intermediate Statistics, 5th ed. New York and London: Routledge, Taylor & Francis Group, 2015, pp. 109–143.
Published
2018-11-01
How to Cite
Bobkova, E., & Parfianovich, E. (2018). NEURAL NETWORKS FOR FORECASTING AND MODELING TRAINING IN TRACK-AND-FIELD ATHLETICS. Human. Sport. Medicine, 18(S), 115-119. https://doi.org/10.14529/hsm18s16
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Section
Sports training
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