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      Application of Multilayer Neural Network in Sports Psychology

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      Scientific Programming
      Hindawi Limited

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          Abstract

          The research on multilayer neural network theory has developed rapidly in recent years. It has parallel processing capabilities and fault tolerance and has aroused the interest of many researchers. The neural network has made great progress in the field of control, especially in model identification and control. It has been quickly applied in the fields of device design, optimized operation, and fault analysis and diagnosis. Neural network control, as an automated control technology in the 21st century, has been fully proved by theories and practices at home and abroad, and it is very useful in complex process control. Sports psychology is a discipline that studies the psychological characteristics and laws of people engaged in sports, and it is also a new development in sports science. The main task of sports psychology is to study people’s psychological processes when participating in sports, such as feeling, perception, appearance, thinking, memory, emotion, and characteristics of will and its role and significance in sports. An important feature of multilayer neural networks is to achieve results that match the expected output through network learning. It has strong self-learning, self-adaptability, and fault tolerance. The multilayer neural network system evaluation method is unique with its extraordinary ability to deal with complex nonlinear problems and does not involve human intervention. This article presents a multilayer neural network algorithm, which classifies the samples of athletes, and studies the physical education training process, the psychological characteristics of related personnel in sports competitions, such as the psychological characteristics of the formation of sports skills, and the psychological training of athletes before the game.

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          Most cited references23

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          Kolmogorov's theorem and multilayer neural networks

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            ECNN: evaluating a cluster-neural network model for city innovation capability

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              Super efficiency SBM-DEA and neural network for performance evaluation

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Scientific Programming
                Scientific Programming
                Hindawi Limited
                1875-919X
                1058-9244
                January 7 2022
                January 7 2022
                : 2022
                : 1-7
                Affiliations
                [1 ]Guangdong Industry Polytechnic, Guangzhou 510300, China
                Article
                10.1155/2022/3692428
                7528d7a1-8c9a-4e6f-817e-13957179b2dc
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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