scholarly journals The Rehabilitation Training Simulation of High Difficulty Movement and Sports Strain Site Based on Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Xiaojie Zhang ◽  
Zhengda Ma ◽  
Yongming Sun ◽  
Yanle Hu
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Sports Injury ◽  
Rehabilitation Training ◽  
Minimum Compliance ◽  
Training Simulation ◽  
The Difference ◽  
Snow Sports

We study the rehabilitation training of damaged parts of ice and snow sports clock and ensure the physical safety of athletes. The results show that the RBF neural network updates the center, weight, and width of the radial basis function, and the predicted maximum compliance is 99%, and the minimum compliance is 93%. After many analysis times, the prediction results show that the difference between the predicted degree of conformity and the actual results is less than 8%. The RBF neural network is trained according to the risk database of sports injury, and the RBF neural network will output corresponding values to realize sports injury estimation. The experimental results show that the designed model has high precision and efficiency.

scholarly journals Rehabilitation Educational Design for Children with Autism Based on the Radial Basis Function Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yueer Qi ◽  
Jia-Xuan Han
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Evaluation Method ◽  
Rbf Neural Network ◽  
Children With Autism ◽  
Design Evaluation ◽  
Weighting Method ◽  
Rehabilitation Training ◽  
Radial Basis

Children with autism need appropriate educational toys to assist rehabilitation training, so as to inhibit the development of autism. Toys and related treatments for children with autism can alleviate some of the deficits of children with autism. By using toys as stimuli and various sensations obtained by children with autism or toys as a result of reinforcement, the improvement of certain capabilities expected by related therapies can be achieved through the process of stimulation and reinforcement. However, in the process of pediatrics toy development, it is difficult for toy designers to assess whether the purpose of stimulation and reinforcement can be achieved. There are many factors that affect the design of rehabilitation toys. The industry has not formed a unified design evaluation standard, and the effects of product rehabilitation training are uneven. A method based on the radial basis function (RBF) neural network was proposed in this research to study the rehabilitation design and evaluation of rehabilitation toys for children with autism. Firstly, according to the three demand indicators for the evaluation of rehabilitation training for children with autism, that is, “useful, educational, and entertaining,” the analytic network process (ANP) method was chosen as the weighting method for determining each indicator in the overall evaluation. The RBF neural network rehabilitation model for children with autism was designed and evaluated. The maximum error of the model was less than 10%. The evaluation method was objective and reasonable, so as to provide a reference for the more accurate design evaluation, purchase, and development of rehabilitation toys for children with autism.

scholarly journals Early Warning Model of Sports Injury Based on RBF Neural Network Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fuxing He
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Data Analysis ◽  
Rbf Neural Network ◽  
Sports Injury ◽  
Big Data Analysis ◽  
Assessment Model ◽  
Estimation Model ◽  
Data Network ◽  
Model Based

Sports injury is a common problem in athletes’ training. The sports injury assessment model is a physical method to determine the sports injury attributes of specific parts by predicting and evaluating the risk of sports injury. In this paper, we use a neural network to realize big data analysis of sports injury data. Big data network is a method of capturing Internet information by means of cloud computing, which is usually used in the construction of Wan and LAN. This paper analyzes the source of sports risk and the main injury factors, designs the sports injury estimation model based on big data analysis, establishes a new assessment model based on RBF neural network, and builds the big data network environment required for the model operation by improving the topological structure, combining big data and deep neural network. In the built environment, the risk assessment of sports injury can be completed by determining the risk source and identifying the risk factors. The realization of the model can be constrained by the uncertainty conditions so that it can achieve a good operation state.

scholarly journals Optimization and Simulation of Enterprise Management Resource Scheduling Based on the Radial Basis Function (RBF) Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ye Wu ◽  
Xiaowen Sun
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Scientific Basis ◽  
Enterprise Management ◽  
Ahp Method ◽  
Radial Basis ◽  
Optimization And Simulation

In the human resource system of modern enterprises, human-post matching big data occupies an important irreplaceable position. With the deepening of the reform of state-owned enterprises, some shortcomings of human-post matching big data have become prominent. The purpose of this article is to solve the current state-owned enterprises. There are a variety of problems with big data in the enterprise, and an effective method is found that can accurately evaluate the degree of human-job matching in state-owned enterprises and provide a scientific basis for the manager of talent and resource allocation to make more rational decisions. Through the radial basis function (RBF) neural network-based big data model of human-post matching evaluation of state-owned enterprises, we scientifically and effectively evaluate the matching degree of the quality and ability of the personnel with the relevant requirements of the position and then help the company to adjust the personnel at any time changes in positions to maximize the efficiency of human resources. In this paper, considering the actual situation of the enterprise, the RBF neural network and the analytic hierarchy process (AHP) method are used comprehensively. Firstly, the AHP is used to obtain the weight of each evaluation index in the human-post matching index system. At the same time, the artificial neural network theory is self-adapting. Learning is helpful to solve the problem that the AHP method is too subjective. The two learn from each other’s strong points and combine their weaknesses organically to increase the convenience and effectiveness of evaluation.

Research on Intelligent Diagnosis Technology of Transformer Fault

2013 ◽  
Vol 385-386 ◽  
pp. 589-592
Author(s):  
Hong Qi Wu ◽  
Xiao Bin Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Intelligent Diagnosis ◽  
Rbf Network ◽  
Working Principle ◽  
Research On Application ◽  
Transformer Fault

In order to improve the diagnosis rates of transformer fault, a research on application of RBF neural network is carried out. The structure and working principle of radial basis function (RBF) neural network are analyzed and a three layer RBF network is also designed for transformer fault diagnosis. It is proved by MATLAB experiment that RBF neural network is a strong classifier which is used to diagnose transformer fault effectively.

The Research Based on RBF Neural Network in the Power of Prediction of Grain Depot

2012 ◽  
Vol 182-183 ◽  
pp. 1358-1361
Author(s):  
Le Xiao ◽  
Min Peng Hu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Electricity Consumption ◽  
Nonlinear Time Series ◽  
Modeling And Prediction ◽  
Radial Basis

According to the fact that the use of electricity in grain depot is nonlinear time series, the article introduces the prediction model of electricity based on Radial Basis Function Neural Network, and conducts the modeling and prediction by adopting the historical electricity consumption of a typical grain depot. As the result of simulation shows, the model obtains better forecasting results in grain depot electricity.

scholarly journals A Novel Evaluation Model for Urban Smart Growth Based on Principal Component Regression and Radial Basis Function Neural Network

2019 ◽  
Vol 11 (21) ◽  
pp. 6125
Author(s):  
Lianyan Li ◽  
Xiaobin Ren
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Evaluation Model ◽  
Smart Growth ◽  
Principal Component Regression ◽  
Principal Component ◽  
Self Organizing Map ◽  
Radial Basis

Smart growth is widely adopted by urban planners as an innovative approach, which can guide a city to develop into an environmentally friendly modern city. Therefore, determining the degree of smart growth is quite significant. In this paper, sustainable degree (SD) is proposed to evaluate the level of urban smart growth, which is established by principal component regression (PCR) and the radial basis function (RBF) neural network. In the case study of Yumen and Otago, the SD values of Yumen and Otago are 0.04482 and 0.04591, respectively, and both plans are moderately successful. Yumen should give more attention to environmental development while Otago should concentrate on economic development. In order to make a reliable future plan, a self-organizing map (SOM) is conducted to classify all indicators and the RBF neural network-trained indicators are separate under different classifications to output new plans. Finally, the reliability of the plan is confirmed by cellular automata (CA). Through simulation of the trend of urban development, it is found that the development speed of Yumen and Otago would increase slowly in the long term. This paper provides a powerful reference for cities pursuing smart growth.

scholarly journals Upset Prediction in Friction Welding Using Radial Basis Function Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Wei Liu ◽  
Feifan Wang ◽  
Xiawei Yang ◽  
Wenya Li
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Radial Basis Function ◽  
Basis Function ◽  
Welded Joints ◽  
Friction Welding ◽  
Rbf Neural Network ◽  
Finite Element Simulations ◽  
Welding Parameters ◽  
Radial Basis

This paper addresses the upset prediction problem of friction welded joints. Based on finite element simulations of inertia friction welding (IFW), a radial basis function (RBF) neural network was developed initially to predict the final upset for a number of welding parameters. The predicted joint upset by the RBF neural network was compared to validated finite element simulations, producing an error of less than 8.16% which is reasonable. Furthermore, the effects of initial rotational speed and axial pressure on the upset were investigated in relation to energy conversion with the RBF neural network. The developed RBF neural network was also applied to linear friction welding (LFW) and continuous drive friction welding (CDFW). The correlation coefficients of RBF prediction for LFW and CDFW were 0.963 and 0.998, respectively, which further suggest that an RBF neural network is an effective method for upset prediction of friction welded joints.

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