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Optimization analysis of football match prediction model based on neural network

  • S.I: Cognitive-inspired Computing and Applications
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Abstract

How to build a football match prediction model and use scientific methods to solve the prediction problem has become a key point in the application of artificial intelligence in the sports industry. In this paper, we choose a BP neural network model that is powerful in processing nonlinear data to perform research. According to the demand, this paper constructs a gray fuzzy prediction model based on neural network, a gray extreme learning machine prediction model, and a gray fuzzy extreme learning machine prediction combination model based on neural network. Moreover, this paper tests the neural network model by comparing actual results with predicted results. In addition, by predicting and analyzing the football league data, this article tests the three models in terms of match result prediction accuracy, data processing speed, data transmission accuracy, match analysis scores, etc., and uses statistical analysis methods to process data, and uses intuitive statistical graphs to obtain the processing results. The research results show that the gray fuzzy extreme learning machine prediction combination model based on neural network constructed in this paper can retain the advantages of a single model and effectively improve the prediction accuracy of the model and the performance of the system.

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Acknowledgements

The study was supported by “2017 Liaoning Province Higher College Basic Scientific Research Project, (Grant No. WQN2017ST03)and (Grant No. WQN2017ST07)”.

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Authors and Affiliations

  1. Wushu and Dance Department, Shenyang Sport University, Shenyang, 110102, China

    Shuo Guan

  2. Management and Communication Department, Shenyang Sport University, Shenyang, 110102, China

    Xiaochen Wang

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  1. Shuo Guan
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  2. Xiaochen Wang
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Correspondence to Xiaochen Wang.

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Guan, S., Wang, X. Optimization analysis of football match prediction model based on neural network. Neural Comput & Applic 34, 2525–2541 (2022). https://doi.org/10.1007/s00521-021-05930-x

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