scholarly journals RESEARCH ON ATHLETES’ PSYCHOLOGICAL REGULATION ABILITY BASED ON BACK PROPAGATION (BP) NEURAL NETWORK ALGORITHM

2021 ◽  
Vol 27 (spe2) ◽  
pp. 83-86
Author(s):  
Yun Tan ◽  
Guoqing Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Psychological Control ◽  
Back Propagation ◽  
Rotation Matrix ◽  
Psychological State ◽  
Advantages And Disadvantages ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Game State

ABSTRACT Athletes’ psychological control ability directly affects competitions. Therefore, it is necessary to supervise the athletes’ game psychology. Athletes’ game state supervision model is constructed through the facial information extraction algorithm. The homography matrix and the calculation method are introduced. Then, two methods are introduced to solve the rotation matrix from the homography matrix. After the rotation matrix is solved, the method of obtaining the facial rotation angle from the rotation matrix is introduced. The two methods are compared in the simulation data, and the advantages and disadvantages of each algorithm are analyzed to determine the method used in this paper. The experimental results show that the model prediction accuracy reaches 70%, which can effectively supervise the psychological state of athletes. This research study is of great significance to improve the performance of athletes in competitions and improve the application of back propagation (BP) neural network algorithm.

Research and Realization of Hardware Back-Propagation Neural Network Based on FPGA

2013 ◽  
Vol 333-335 ◽  
pp. 2469-2474
Author(s):  
Fei Guo ◽  
Xiao Luo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Variable Structure ◽  
Maximum Error ◽  
Back Propagation Neural Network ◽  
Neural Network Modeling ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Timing Simulation

In order to meet the requirements of real-time and embedded of industrial field, a reconfigurable Back-Propagation neural network based on FPGA has been implemented on Xilinx's Spartan-3E (XC3S250E) chip which has 250000 gate. First the optimal network structure and weights were gotten by a variable structure of BP neural network algorithm. Then an improved hardware approaching method of excitation function was put forward, and the maximum error was 1.58% by simulation and comparative analysis on the error. Finally hardware co-imitation and timing simulation was token based on a reasonable choice of data accuracy, and then the hardware BP neural network algorithm was been downloaded and implemented on FPGA. This method has better accuracy and speed, it is an effective method of BP neural network modeling based on hardware, and lays the foundation for the hardware realization of other neural network and embedded image processing.

Neural network-based optimization model for sprint data collection

2021 ◽  
pp. 1-11
Author(s):  
Guangfei Luo
Keyword(s):  
Neural Network ◽  
Data Collection ◽  
Data Acquisition ◽  
Bp Neural Network ◽  
Optimization Model ◽  
Balance Control ◽  
Performance Parameters ◽  
Advantages And Disadvantages ◽  
Network Algorithm ◽  
Neural Network Algorithm

Sprint data has the characteristics of quality and continuity, but due to the limitations of optimization algorithm, the existing sprint data acquisition optimization model has the problem of low optimization performance parameters. Therefore, a data acquisition control optimization model based on neural network is proposed. This paper analyzes the advantages and disadvantages of neural network algorithm, combined with the sprint data collection optimization requirements, introduces BP neural network algorithm, based on this, uses multiple sensors, based on baud interval balance control to collect sprint data, applies BP neural network algorithm to compress, integrate and classify sprint data, realizes the sprint data collection and optimization. The experimental results show that the optimization performance parameters of the model are large, which fully shows that the model has good data acquisition optimization performance.

scholarly journals Analysis of Sports Performance Prediction Model Based on GA-BP Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jinjuan Wang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Performance Prediction ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Training Model ◽  
Sports Performance ◽  
Advantages And Disadvantages ◽  
Network Algorithm ◽  
Neural Network Algorithm

There are many factors that affect athletes’ sports performance in sports competitions. The traditional sports performance prediction method is difficult to obtain more accurate sports performance prediction results and corresponding data analysis in a short time, which is not conducive for coaches to formulate targeted and scientific training sprint plans for athletes’ problems. Therefore, based on GA-BP neural network algorithm, this paper constructs a sports performance prediction model and carries out experiments and analysis. The experimental results show that GA-BP neural network algorithm has a faster convergence speed than BP neural network and can achieve the expected error accuracy in a shorter time, which overcomes the problems of the BP neural network. At the same time, different from the previous models, GA-BP neural network algorithm can get the athlete training model according to the relationship between quality training indicators and special sports training results, which can more intuitively show the advantages and disadvantages of athletes. In the final sports performance prediction results, GA-BP neural network prediction results have higher accuracy, better stability, better prediction effect, and higher application value than BP neural network.

An Improved BP Neural Network Algorithm and its Application

2014 ◽  
Vol 543-547 ◽  
pp. 2120-2123 ◽  
Author(s):  
Ming Jun Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Ant Colony Algorithm ◽  
Back Propagation ◽  
Indicator System ◽  
Global Optimum ◽  
Bp Algorithm ◽  
Nonlinear Simulation ◽  
Network Algorithm ◽  
Neural Network Algorithm

Back-propagation (BP) neural network algorithm is currently used most widely and grows fastest for its powful nonlinear simulation capability. However BP neural network is so easy to fall into local minima that it cant find the global optimum which limits its application in many fields. The paper, taking tax innovation teaching evaluation for example, advances a new evaluation algorithm based on improved BP neural network algorithm. Firstly an evaluation indicator system of tax major innovation teaching is designed through analyzing the specific characteristics of innovation teaching requirements. Secondly, in order to overcome the shortages of low convergence speed of original BP neural network algorithm, the paper improves BP algorithm through integrating BP algorithm and ant colony algorithm, ant improving the overall search method of integrated algorithm. Thirdly data from three universities are taken for examples to verify the validity and feasibility of the model and the experimental results show that the model can evaluate university innovation teaching practically.

scholarly journals Woodworking Tool Wear Condition Monitoring during Milling Based on Power Signals and a Particle Swarm Optimization-Back Propagation Neural Network

2021 ◽  
Vol 11 (19) ◽  
pp. 9026
Author(s):  
Weihang Dong ◽  
Xianqing Xiong ◽  
Ying Ma ◽  
Xinyi Yue
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Condition Monitoring ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Arithmetic Average ◽  
Filtering Method ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Wear Condition

In the intelligent manufacturing of furniture, the power signal has the characteristics of low cost and high accuracy and is often used as a tool wear condition monitoring signal. However, the power signal is not very sensitive to tool wear conditions. The present work addresses this issue by proposing a novel woodworking tool wear condition monitoring method that employs a limiting arithmetic average filtering method and particle swarm optimization (PSO)-back propagation (BP) neural network algorithm. The limiting arithmetic average filtering method was used to process the power signal and extracted the features of the woodworking tool wear conditions. The spindle speed, depths of milling, features and tool wear conditions were used as sample vectors. The PSO-BP neural network algorithm was used to establish the monitoring model of the woodworking tool wear condition. Experiments show that the proposed limiting arithmetic average filtering method and PSO-BP neural network algorithm can accurately monitor the woodworking tool wear conditions under different milling parameters.

A time simulated annealing-back propagation algorithm and its application in disease prediction

2018 ◽  
Vol 32 (25) ◽  
pp. 1850303 ◽  
Author(s):  
Fang Hu ◽  
Mingzhu Wang ◽  
Yanhui Zhu ◽  
Jia Liu ◽  
Yalin Jia
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Bp Algorithm ◽  
Disease Prediction ◽  
Back Propagation Algorithm ◽  
Network Algorithm ◽  
Learning Rates ◽  
Neural Network Algorithm

In this paper, based on the Back Propagation (BP) neural network algorithm, we introduce the idea of the Simulated Annealing (SA), and then propose a new neural network algorithm: Time Simulated Annealing-Back Propagation (TSA-BP) algorithm. The proposed algorithm can improve the convergence rate and numerical stability. By using this proposed algorithm, the learning rates and initial weights in the BP neural network could be easily adjusted. We show that the TSA-BP algorithm could reduce the errors caused by human-made factors. Several numerical experiments have been tested by using different disease data. Furthermore, we compared the TSA-BP algorithm to the other existing, well-known algorithms. Numerical results show higher accuracy and efficiency of the TSA-BP algorithm.

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