scholarly journals Development of Basketball Tactics Basic Cooperation Teaching System Based on CNN and BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lin Hua ◽  
Guangyu Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Convolutional Neural Network ◽  
Bp Neural Network ◽  
Control Group ◽  
Gradient Descent Method ◽  
Teaching System ◽  
Event Classification ◽  
Basketball Game ◽  
Artificial Neural

The traditional basketball teaching mode cannot meet the needs of students for the basic cooperation of basketball tactics. Therefore, a basic cooperation teaching system of basketball tactics based on artificial neural network is studied and designed. The system has a professional basketball game video tactical learning module. The events in the basketball game video are classified through a convolutional neural network and combined with the explanation of teachers to make the students have an intuitive understanding of the basic cooperation of basketball tactics and then design the basketball game module based on a BP neural network to provide students with an online basketball tactics training platform. Finally, the teacher scores the performance of the actual on-site training students in the basic cooperation of basketball tactics through the tactical scoring module on the system. The results show that after the introduction of global and collective motion patterns, the classification accuracy of the convolutional neural network is improved by 22.48%, which has significant optimization. The average accuracy of basketball game video event classification is 62.35%, and the accuracy of snatch event classification is improved to 95.28%. The recognition rate of the BP neural network combined with momentum gradient descent method is 75%, the number of weight adjustment is less, and the memory is small while ensuring fast running speed. Students who accept the basic basketball tactics cooperation teaching system based on the artificial neural network for basketball teaching have an overall score of 27.99 ± 2.11 points The overall score of exchange defense cooperation was 24.12 ± 2.03, which was higher than that of the control group. The above results show that the basketball tactical basic cooperation teaching system based on the artificial neural network has a good teaching effect in improving students’ basketball tactical basic cooperation ability.

Based on BP Neural Network Discrete Data Forecast

2011 ◽  
Vol 50-51 ◽  
pp. 977-981 ◽  
Author(s):  
Jing Wang ◽  
Guo Li Wang ◽  
Jian Hui Wu ◽  
Yu Su
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Statistical Methods ◽  
Bp Neural Network ◽  
Model Fitting ◽  
Predictive Ability ◽  
Discrete Data ◽  
Engineering System ◽  
Forecast Performance ◽  
Artificial Neural

Artificial neural network is based on human brain structure and operational mechanism based on knowledge and understanding of its structure and behavior of simulated an engineering system. BP artificial neural network is an important component of neural networks, as it can on the linear or nonlinear multivariable without preconditions in the case of statistical analysis, with the traditional statistical methods, analysis of the variables need to be consistent with certain conditions compared to its own advantage. The BP neural network does not need the precise mathematical model, does not have any supposition request to the material itself. Its processing non-linear problem's ability is stronger than traditional statistical methods. This article uses two groups of data to establish the BP neural network model separately, and carries on the comparison to the model fitting ability and the forecast performance, discovered BP neural network when data distribution relative centralism fits ability, forecasts the stable property. But the predictive ability is unable in the discrete data application to achieve anticipated ideally.

Resistivity-depth imaging of airborne transient electromagnetic method based on an artificial neural network

2020 ◽  
Author(s):  
Jifeng Zhang ◽  
Bing Feng ◽  
Dong Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Apparent Resistivity ◽  
Electromagnetic Method ◽  
Electromagnetic Response ◽  
Model Parameters ◽  
Transient Electromagnetic Method ◽  
Transient Electromagnetic ◽  
Artificial Neural

<p>An artificial neural network, which is an important part of artificial intelligence, has been widely used to many fields such as information processing, automation and economy, and geophysical data processing as one of the efficient tools. However, the application in geophysical electromagnetic method is still relatively few. In this paper, BP neural network was combined with airborne transient electromagnetic method for imaging subsurface geological structures.</p><p>We developed an artificial neural network code to map the distribution of geologic conductivity in the subsurface for the airborne transient electromagnetic method. It avoids complex derivation of electromagnetic field formula and only requires input and transfer functions to obtain the quasi-resistivity image section. First, training sample set, which is airborne transient electromagnetic response of homogeneous half-space models with the different resistivity, is formed and network model parameters include the flight altitude and the time constant, which were taken as input variables of the network, and pseudo-resistivity are taken as output variables. Then, a double hidden layer BP neural network is established in accordance with the mapping relationship between quasi-resistivity and airborne transient electromagnetic response. By analyzing mean square error curve, the training termination criterion of BP neural network is presented. Next, the trained BP neural network is used to interpret the airborne transient electromagnetic responses of various typical layered geo-electric models, and it is compared with those of the all-time apparent resistivity algorithm. After a lot of tests, reasonable BP neural network parameters were selected, and the mapping from airborne TEM quasi-resistivity was realized. The results show that the resistivity imaging from BP neural network approach is much closer to the true resistivity of model, and the response to anomalous bodies is better than that of all-time apparent resistivity numerical method. Finally, this imaging technique was use to process the field data acquired by the airborne transient method from Huayangchuan area. Quasi-resistivity depth section calculated by BP neural network and all-time apparent resistivity is in good agreement with the actual geological situation, which further verifies the effectiveness and practicability of this algorithm.</p>

scholarly journals Automation of systematic reviews through artificial neural network modeling and machine learning: A methodological protocol report (Preprint)

2020 ◽  
Author(s):  
Gabriel Ferraz Ferreira Sr ◽  
Marcos Gonçalves Quiles Sr ◽  
Tiago Santana Nazare Sr ◽  
Solange Oliveira Rezende ◽  
Marcelo Demarzo Sr
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Systematic Review ◽  
Artificial Neural Network ◽  
Systematic Reviews ◽  
Selection Process ◽  
Automated System ◽  
Neural Network Modeling ◽  
Control Group ◽  
Artificial Neural

UNSTRUCTURED Background: A systematic review can be defined as a summary of the evidence found in the literature via a systematic search in the available scientific databases. One of the steps involved is article selection, which is typically a laborious task. Machine learning and artificial intelligence can be important tools in automating this step, thus aiding researchers. The aim of this study is to create models based on an artificial neural network system and machine learning to automate the article selection process in systematic reviews in the area of Mindfulness. Methods: The study will be performed using R programming software. The system will consist of six main steps: 1) data import; 2) exclusion of duplicates; 3) exclusion of nonarticles; 4) article reading and model creation using artificial neural networks; 5) comparison of the models; and 6) system sharing. We will choose the 10 most relevant systematic reviews published in the fields of “Mindfulness and Health Promotion” and “Orthopedics and Traumatology” (control group) to serve as a test of the effectiveness of the article selection. The final results for these two fields will be compared. Conclusion: An automated system with a modifiable sensitivity will be created to select scientific articles in systematic review that can be expanded to various fields. We will disseminate our results and models through the “Observatory of Evidence” in public health, an open and online platform that will assist researchers in systematic reviews.

Resistivity-depth Imaging with the Airborne Transient Electromagnetic Method Based on an Artificial Neural Network

2020 ◽  
Vol 25 (3) ◽  
pp. 355-368
Author(s):  
Bing Feng ◽  
Ji-feng Zhang ◽  
Dong Li ◽  
Yang Bai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Apparent Resistivity ◽  
Electromagnetic Method ◽  
Electromagnetic Response ◽  
Model Parameters ◽  
Transient Electromagnetic Method ◽  
Transient Electromagnetic ◽  
Artificial Neural

We developed an artificial neural network to map the distribution of geologic conductivity in the earth subsurface using the airborne transient electromagnetic method. The artificial neural network avoids the need for complex derivations of electromagnetic field formulas and requires only input and transfer functions to obtain a quasi-resistivity image. First, training sample set from the airborne transient electromagnetic response of homogeneous half-space models with different resistivities was formed, and network model parameters, including the flight altitude, time constant, and response amplitude, were determined. Then, a double-hidden-layer back-propagation (BP) neural network was established based on the mapping relationship between quasi-resistivity and airborne transient electromagnetic response. By analyzing the mean square error curve, the training termination criterion of the BP neural network was determined. Next, the trained BP neural network was used to interpret the airborne transient electromagnetic responses of various typical layered geo-electric models, and the results were compared with that from the all-time apparent resistivity algorithm. The comparison indicated that the resistivity imaging from the BP neural network approach was much closer to the true resistivity of the model, and the response to anomalous bodies was better than that from an all-time apparent resistivity. Finally, this imaging technique was used to process field data acquired by employing the airborne transient method from the HuaYin survey area. Quasi-resistivity depth sections calculated with the BP neural network and the actual geological situation were in good.

Prediction of Mine Inrush Water Based on BP Neural Network Method

2014 ◽  
Vol 989-994 ◽  
pp. 1814-1820 ◽  
Author(s):  
Ai Jun Shao ◽  
Qing Xin Meng ◽  
Shi Wen Wang ◽  
Ying Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Mine Water ◽  
Influential Factors ◽  
Water Inflow ◽  
Impact Factors ◽  
Artificial Neural ◽  
Inrush Water ◽  
The Impact

Based on predictions of the mine inflow of water and the complexity of influential factors, a method of BP neural network is put forward for mine inrush water prediction in this paper. We chose proper impact factors and establish non-linear artificial neural network prediction model after analyzed the impact factors of mine water inflow in Shandong Heiwang iron, and also made one prediction with normal mine water inflow during the iron mining operation. It turned out that the result can match with the actual prediction data, which make it possible to predict the mine water inflow with the prediction of Artificial Neural Network.

Application of Artificial Neural Network in Corporate Financial Risk Early-Warning

2013 ◽  
Vol 336-338 ◽  
pp. 2476-2479 ◽  
Author(s):  
Hong Xiao Zhou ◽  
Sai Hua Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Warning ◽  
Bp Neural Network ◽  
Financial Risk ◽  
Financial Data ◽  
Early Warning Model ◽  
Risk Warning ◽  
Artificial Neural ◽  
Warning Model

The traditional financial risk warning model are all based on probability theory and statistical analysis, but the precisions of the results are usually not satisfied in practice. This paper studies the application of artificial neural network in corporate financial risk early-warning. It designs an early warning model of financial risk based on BP neural network. And then selects financial data from 30 enterprises as samples to train and test the network. The result indicates that the risk early warning model is very effective. It can solve some problems of the traditional early warning methods such as difficult to deal with highly non-linear and lack of adaptive capacity.

Artificial Neural Network Analysis Method of Concrete Performance Based on Virtual Data Samples

2010 ◽  
Vol 34-35 ◽  
pp. 462-466
Author(s):  
Jun Wei Song ◽  
Yan Shi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Influence Factors ◽  
Forecast Performance ◽  
Virtual Samples ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural ◽  
Predicted Values ◽  
The Relationship

The relationship between concrete performance and influence factors is uncertain and nonlinear. Accordingly, present BP neural network and virtual samples are presented to predict concrete performance in this paper. At first neural network and matters which need attention are introduced, And frost resistance forecasting model and impermeability model are built up, which are three-tier BP neural network of 6-13-2,4-9-1.The results show that the predicted values are ideal, and artificial neural network as one of the methods to forecast performance of concrete is appropriate.

Prediction of High Cycle Fatigue Property of Ti-6Al-4V Alloy Using Artificial Neural Network

2016 ◽  
Vol 849 ◽  
pp. 360-367
Author(s):  
Ye Man Zhao ◽  
Hong Chao Kou ◽  
Wei Wu ◽  
Ying Deng ◽  
Bin Tang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cyclic Loading ◽  
Bp Neural Network ◽  
High Cycle Fatigue ◽  
Volume Fraction ◽  
Fatigue Property ◽  
Loading Frequency ◽  
Cycle Fatigue ◽  
Artificial Neural

In this paper, the relationship between microstructure, parameters of cyclic loading and high cycle fatigue property of Ti-6Al-4V alloy was established by artificial neural network (ANN) modeling. The back propagation (BP) neural network and radial basis function (RBF) neural network were established by MATLAB. The input parameters of these models were the primary α volume fraction, primary α size, cyclic loading frequency and stress ratio. The output parameter was high cycle fatigue strength. The neural networks were trained with dataset collected from the literature. The prediction results showed that both of the networks have good generalization ability. In addition, the BP neural network with Levenberg-Merquardt (LM) learning algorithm has better fault tolerance and versatility in dealing with high cycle fatigue property, which is able to predict the high cycle fatigue property with a high accuracy.

Study on the Effectiveness Evaluation of the Weapon Equipment Based on Artificial Neural Network

2014 ◽  
Vol 926-930 ◽  
pp. 3262-3265
Author(s):  
Feng Gao ◽  
Fei Song ◽  
Guo Qing Huang ◽  
Mao Yang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Effectiveness Evaluation ◽  
Matlab Toolbox ◽  
New Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Better Than

A new approach to weapons and equipment effectiveness evaluation based on artificial neural network (ANN) performs better than traditional method, which is in view of the complex relationship between the effectiveness and many factors that influence the evaluation. The structure and learning algorithm of BP neural network is evaluated the fighters’ air-to-air combat capability. The evaluation is accomplished by a two-layer BP neural network and MATLAB toolbox. The simulation results show that the artificial neural network have better generalization ability and approximation performance for continuous function, which is valuable in weapons and equipment effectiveness evaluation application.

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