scholarly journals Analysis of Effectiveness and Performance Prediction of Sports Flipped Classroom Teaching Based on Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Wei Xu ◽  
Wenying Xiong ◽  
Zhe Shao ◽  
Yun Li
Keyword(s):  
Neural Network ◽  
College Students ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Physical Education ◽  
Flipped Classroom ◽  
Extreme Values ◽  
Sports Performance ◽  
Swarm Optimization ◽  
The Neural Network

Traditional physical education methods are unable to meet this requirement due to the practical nature of sports skill teaching. As a result, as the times demanded, the flipped classroom based on neural network technology arose. It has the potential to not only promote the modernization of physical education but also to ensure that it has a positive educational impact. This is a mode of instruction. Furthermore, colleges and universities are increasingly focusing on college students’ overall quality development. A method for predicting college students’ sports performance using a particle swarm optimization neural network is proposed to accurately predict sports performance and provide a reliable analysis basis for the establishment of sports teaching goals. Neural networks are used in the model. The particle swarm optimization algorithm optimizes the variance and weights of the neural network to improve the accuracy of college students’ sports performance predicted by the neural network by updating the particle position and speed through the two extreme values of individual extreme values and global extreme values. Teachers always play the role of the facilitator and helper in the teaching process, which realizes the transformation of teachers’ and students’ self-positioning, allows students to better play the lead role, and stimulates students’ interest in learning.

Using Particle Swarm Optimization Algorithm as an Optimization Tool Within Developed Neural Networks

2018 ◽  
pp. 215-244
Author(s):  
Goran Klepac
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Optimization Algorithm ◽  
Recommendation System ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Combination ◽  
Swarm Optimization

Developed neural networks as an output could have numerous potential outputs caused by numerous combinations of input values. When we are in position to find optimal combination of input values for achieving specific output value within neural network model it is not a trivial task. This request comes from profiling purposes if, for example, neural network gives information of specific profile regarding input or recommendation system realized by neural networks, etc. Utilizing evolutionary algorithms like particle swarm optimization algorithm, which will be illustrated in this chapter, can solve these problems.

Modeling and Optimisation of Xylose Production by Enzymatic Hydrolysis using Neural Network and Particle Swarm Optimization

2015 ◽  
Vol 10 (3) ◽  
pp. 173-178 ◽  
Author(s):  
Nur Atiqah Nurhalim ◽  
Mashitah Mat Don ◽  
Zainal Ahmad ◽  
Dipesh S. Patle
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Enzymatic Hydrolysis ◽  
Particle Swarm ◽  
Agitation Speed ◽  
Process Conditions ◽  
Optimum Conditions ◽  
Swarm Optimization ◽  
The Neural Network ◽  
Hydrolysis Process

Abstract Particle swarm optimization (PSO) method is used for the optimization of an enzymatic hydrolysis process for the production of xylose from rice straw. The enzymatic hydrolysis process conditions such as temperature, agitation speed and concentration of enzyme were optimized by using PSO to obtain the optimum yield of xylose. Data collected from an experimental design using response surface methodology were necessitated to develop the neural network modeling. The neural network model is used as a model in objective function of PSO to predict the optimum conditions, which involved in the enzymatic hydrolysis process. The optimum value is obtained from the performance of the best particle swarm among the optimum conditions in PSO. The predicted optimum values were validated through the experiment of the enzymatic hydrolysis process. The optimum temperature, agitation speed and xylanase concentration is observed to be 50.3°C, 132 rpm and 1.6474 mg/ml, respectively. The optimal yield of xylose is predicted as 0.1845 mg/ml using PSO.

MODELING OF INPUT-OUTPUT RELATIONSHIPS FOR ELECTRON BEAM BUTT WELDING OF DISSIMILAR MATERIALS USING NEURAL NETWORKS

2014 ◽  
Vol 13 (03) ◽  
pp. 1450016 ◽  
Author(s):  
M. N. JHA ◽  
D. K. PRATIHAR ◽  
A. V. BAPAT ◽  
V. DEY ◽  
MAAJID ALI ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Electron Beam ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Input Output ◽  
Swarm Optimization ◽  
Butt Welding

Electron beam butt welding of stainless steel (SS 304) and electrolytically tough pitched (ETP) copper plates was carried out according to central composite design of experiments. Three input parameters, namely accelerating voltage, beam current and weld speed were considered in the butt welding experiments of dissimilar metals. The weld-bead parameters, such as bead width and depth of penetration, and weld strength in terms of yield strength and ultimate tensile strength were measured as the responses of the process. Input-output relationships were established in the forward direction using regression analysis, back-propagation neural network (BPNN), genetic algorithm-tuned neural network (GANN) and particle swarm optimization algorithm-tuned neural network (PSONN). Reverse mapping of this process was also conducted using the BPNN, GANN and PSONN approaches, although the same could not be done from the obtained regression equations. Neural networks were found to tackle the problems of both forward and reverse mappings efficiently. However, neural networks tuned by the genetic algorithm and particle swarm optimization algorithm were seen to perform better than the BPNN in most of the cases but not all.

scholarly journals SELEKSI FITUR ALGORITMA NEURAL NETWORK MENGGUNAKAN PARTICLE SWARM OPTIMIZATION UNTUK MEMPREDIKSI KELAHIRAN PREMATUR

Kilat ◽  
2018 ◽  
Vol 6 (2) ◽  
pp. 106-111
Author(s):  
Redaksi Tim Jurnal
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Preterm Birth ◽  
Sensitivity And Specificity ◽  
Particle Swarm ◽  
Data Set ◽  
Swarm Optimization ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm

Premature birth, defined as delivery in pregnant women with gestation age 20 - 36 weeks. Research related to preterm birth has been done by the researchers by using the neural network method. However such research only showcase about the results of the sensitivity and specificity. The results of research using the method of neural network in predicting preterm birth has a value of the resulting accuracy is still less accurate and only limited to presenting the results of the sensitivity and specificity. In this study produced a model of the neural network algorithm and model of neural network algorithm based on particle swarm optimization to get the architecture in predicting preterm birth and gives a more accurate value for accuracy on a data set of RSUPN Cipto Mangunkusumo , RS Sumber Waras and in its entirety. After you are done testing with two models of neural network algorithms and neural network algorithm based on particle swarm optimization and the results obtained are the neural network algorithm generates value accuracy of 94,60%, 96,40%, 91,33%, and AUC values of 0,973, 0,982, 0,953, however, after the addition of the neural network algorithm based on particle swarm optimization value accuracy of 95,20%, 96,80%, 92,40% and AUC values of 0,979 , 0,987, 0,965. So both of these methods has the distinction of accuracy which amounted to 0.60%, 0.40%, 1.07% and AUC value difference of 0.006, 0.005, 0.012.

scholarly journals An Internet of Things-Oriented Adaptive Mutation PSO-BPNN Algorithm to Assist the Construction of Entrepreneurship Evaluation Models for College Students

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Huaxiang Fu
Keyword(s):  
Neural Network ◽  
College Students ◽  
Particle Swarm Optimization ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Particle Swarm ◽  
Adaptive Mutation ◽  
Support Vector ◽  
Swarm Optimization

In this paper, the IoT-based adaptive mutation PSO-BPNN algorithm is used to conduct in-depth research and analysis of the entrepreneurship evaluation model for college students and practical applications. This paper details the principle, implementation, and characteristics of each BP algorithm and PSO algorithm. When classifying college students’ entrepreneurship evaluation based on BP neural network, because BP algorithm is a local optimization-seeking algorithm, it is easy to fall into local minima in the training phase of the network and the convergence speed is slow, which leads to the reduction of classifier recognition rate. To address the above problems, this paper proposes the algorithm of PSO optimized BP neural network (PSO-BPNN) and establishes a classification and recognition model based on this algorithm for college students’ entrepreneurship evaluation. The predicted values obtained from the particle swarm optimization neural network model are used to calculate the gray intervals, and the modeling samples are further screened using the gray intervals and the correlation principle, while the hyperspectral particle swarm optimization neural network model of soil organic matter based on the gray intervals is established afterward; and the estimation results are compared and analyzed with those of traditional modeling methods. The results showed that the coefficient of determination of the gray interval-based particle swarm optimization neural network model was 0.8826, and the average relative error was 3.572%, while the coefficient of determination of the particle swarm optimization neural network model was 0.853, and the average relative error was 4.34%; the average relative errors of the BP neural network model, support vector machine model, and multiple linear regression model were 8.79%, 6.717%, and 9.9%, respectively. The average relative errors of the BP neural network model, support vector machine model, and multiple linear regression model are 8.79%, 6.717%, and 9.468%, respectively. In general, the entrepreneurial ability of college students is at a good level (83.42 points), among which the entrepreneurial management ability score (84.30 points) and entrepreneurial spirit (84.16 points) are basically the same, while the entrepreneurial technology ability is relatively low (82.76 points), and the evaluation results are further verified by the double case analysis method. The current problems encountered by university students in entrepreneurship are mainly the lack of practicality, which indicates that universities, industries, and national strategy implementation levels are not sufficiently focused and collaborative in entrepreneurship development to varying degrees.

An Efficiency Algorithm for SDCP Program

2014 ◽  
Vol 971-973 ◽  
pp. 1533-1536
Author(s):  
Ning Xiao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Intelligent Algorithm ◽  
Swarm Optimization ◽  
Hybrid Intelligent Algorithm ◽  
Random Simulation ◽  
Training Samples ◽  
Stochastic Particle

For more effectively solving SDCP,in the paper,using BP neural networks to approximate chance function,training samples are produced by random simulation,and a hybrid intelligent algorithm for SDCP combined stochastic particle swarm optimization and BP neural network is proposed.The experimental results show that the algorithm is more preferable.

scholarly journals Concepts, Methods, and Performances of Particle Swarm Optimization, Backpropagation, and Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Leke Zajmi ◽  
Falah Y. H. Ahmed ◽  
Adam Amril Jaharadak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Real World ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Advantages And Disadvantages ◽  
Artificial Neural ◽  
Real World Problems

With the advancement of Machine Learning, since its beginning and over the last years, a special attention has been given to the Artificial Neural Network. As an inspiration from natural selection of animal groups and human’s neural system, the Artificial Neural Network also known as Neural Networks has become the new computational power which is used for solving real world problems. Neural Networks alone as a concept involve various methods for achieving their success; thus, this review paper describes an overview of such methods called Particle Swarm Optimization, Backpropagation, and Neural Network itself, respectively. A brief explanation of the concepts, history, performances, advantages, and disadvantages is given, followed by the latest researches done on these methods. A description of solutions and applications on various industrial sectors such as Medicine or Information Technology has been provided. The last part briefly discusses the directions, current, and future challenges of Neural Networks towards achieving the highest success rate in solving real world problems.

Application of a Optimized Wavelet Neural Networks in Rolling Bearing Fault Diagnosis

2012 ◽  
Vol 190-191 ◽  
pp. 919-922 ◽  
Author(s):  
Yuan Yan Lin ◽  
Bin Wu Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Wavelet Neural Networks ◽  
Rolling Bearings ◽  
Swarm Optimization ◽  
Bearing Fault Diagnosis ◽  
Fault Type

According to the fault type and fault signal of rolling bearing is difficult to predict, the paper proposed a new method to diagnose fault of rolling bearings with the wavelet neural network optimizated by simulated annealing particle swarm optimization. And it was applied to the fault diagnosis of rolling bearing. The experiment shows that this method can reduce the iteration time and improve the accuracy of convergence.

Training oscillatory neural networks using natural gradient particle swarm optimization

Robotica ◽  
2014 ◽  
Vol 33 (7) ◽  
pp. 1551-1567 ◽  
Author(s):  
Hamed Shahbazi ◽  
Kamal Jamshidi ◽  
Amir Hasan Monadjemi ◽  
Hafez Eslami Manoochehri
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Transfer Functions ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Humanoid Robots ◽  
Unknown Parameters ◽  
Swarm Optimization ◽  
Oscillatory Neural Network

SUMMARYIn this paper, a new design of neural networks is introduced, which is able to generate oscillatory patterns in its output. The oscillatory neural network is used in a biped robot to enable it to learn to walk. The fundamental building block of the neural network proposed in this paper is O-neurons, which can generate oscillations in its transfer functions. O-neurons are connected and coupled with each other in order to shape a network, and their unknown parameters are found by a particle swarm optimization method. The main contribution of this paper is the learning algorithm that can combine natural policy gradient with particle swarm optimization methods. The oscillatory neural network has six outputs that determine set points for proportional-integral-derivative controllers in 6-DOF humanoid robots. Our experiment on the simulated humanoid robot presents smooth and flexible walking.

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