A Blocking Prediction for Volleyball Using Neural Networks

Article Preview

Abstract:

Volleyball is a popular exercise. There are not only lots of sports enthusiasts but also many professional athletes. In a variety of tactics and globalization of volleyball sport, the match situation becomes complex and intense a lot in the relative. However, for the professional athletes, the focus of training is still just the specific skills and fitness training, so only doing the traditional training courses will make the athletes more difficult to get the winning. Therefore, in this paper, a new training conception is proposed to enhance the volleyball players successful blocking rate by neural network.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1224-1230

Citation:

Online since:

August 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] H. J. Eom and R. W. Sochutz: Res Q Exerc Sport, 63(1): 11-8 (1992).

Google Scholar

[2] M. S. Chang and C. T. Yao: Analysis of Ace Effect in College Men's Volleyball Games (National Hualien University of Education, Hualien, Taiwan, Sep. 2007, In Chinese).

Google Scholar

[3] M. Xu and S. Dong: Reassembling the Fragmented JPEG Images Based on Sequential Pixel (International Symposium on Computer Network and Multimedia Technology, pp.1-6, Jan. 2009).

DOI: 10.1109/cnmt.2009.5374775

Google Scholar

[4] S. Li, J. T. Kwok and Y. Wang: Pattern Recognition Letters, vol. 23, issue 8, pp.985-997 (2002).

Google Scholar

[5] H. Masoumi, A. Behrad, M. A. Pourmina and A. Roosta: Biomedical Signal Processing and Control, vol. 7, issue 5, pp.429-437 (2012).

DOI: 10.1016/j.bspc.2012.01.002

Google Scholar

[6] Y. Wu, Y. Wu, J. Wang, Z. Yan, L. Qu, B. Xiang and Y. Zhang: Expert Systems with Applications, vol. 38, issue 9, pp.11329-11334 (2011).

DOI: 10.1016/j.eswa.2011.02.183

Google Scholar

[7] S. D. Bekiros: IEEE Transactions on Neural Network, vol. 22, no. 12, pp.2353-2362 (2011).

Google Scholar

[8] C. Langin, H. Zhou, S. Rahimi, B. Gupta and M. Zargham: A Self-Organizing Map and its Modeling for Discoverig Malignant Network Traffic (IEEE Symposium on Computational Intelligence in Cyber Security, pp.122-129, Mar. 2009).

DOI: 10.1109/cicybs.2009.4925099

Google Scholar

[9] A. K. Gulve and D. G. Vyawahare: International Journal of Computer Science and Application, vol. 4, no. 2 (2011).

Google Scholar

[10] P. Sangkatsanee, N. Wattanapongsakorn and C. Charnsripinyo: Computer Communications, vol. 34, no. 18, pp.2227-2235 (2011).

DOI: 10.1016/j.comcom.2011.07.001

Google Scholar

[11] M. Pfeiffer and A. Hohmann: Human Movement Science, vol. 31, no. 2, pp.344-359 (2012).

Google Scholar

[12] T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman and A. Y. Wu: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp.881-892 (2002).

DOI: 10.1109/tpami.2002.1017616

Google Scholar

[13] R. Kwang, Y. Chang, C. K. Loo and M. V. C. Rao: Informatica Slovenia, vol. 32, no. 2, pp.219-225 (2008).

Google Scholar