Combination of Grey System and Neural Network Based Sports Achievement Forecasting Algorithm

DOI

10.2991/ammsa-17.2017.79How to use a DOI?

Keywords

sports achievement; forecasting; grey system; neural network

Abstract

This paper presents two novel forecasting algorithms for sports achievement based on combination of grey model and neural network: (1) GM-NN1: Firstly, the error sequence is obtained by GM(1,1) model using original data sequence of sports achievement, and then in order to gain a forecasting error sequence, the neural network is built up to train the regression of error sequence. This new model corrects the error of GM(1,1) model prediction using neural network, and its accuracy has been significantly improved. (2) GM-NN2: This model uses the partial-data sequence of the original sports achievement data to create partial-data GM(1,1) model group, and build a neural network to establish the nonlinear relationship between the fitted values and original data, the generated network estimates the forecasting development trend of the partial-data GM(1,1) model group, and achieves better results in the medium- and long-term forecast for sports achievement.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiaoyu Zhang
PY  - 2017/05
DA  - 2017/05
TI  - Combination of Grey System and Neural Network Based Sports Achievement Forecasting Algorithm
BT  - Proceedings of the 2017 International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2017)
PB  - Atlantis Press
SP  - 353
EP  - 356
SN  - 1951-6851
UR  - https://doi.org/10.2991/ammsa-17.2017.79
DO  - 10.2991/ammsa-17.2017.79
ID  - Zhang2017/05
ER  -