Mining Mobile Intelligence for Wireless Systems: A Deep Neural Network Approach

This study constructs a new radial basis function-particle swarm optimization neural network (RBFNN-PSO) system, which is applied to the evaluation system of physical education teaching effect. In order to verify the evaluation performance of the RBFNN-PSO system, the traditional RBF neural network system is used as the control, and the training is carried out. The results show that the RBFNN-PSO system can reach the convergence value faster than the traditional RBF neural network system in the training, and the training error is smaller. The results show that the scoring error of RBFNN-PSO system is smaller than that of RBF neural network system, with higher accuracy and smaller error. The experimental results show that the RBFNN-PSO is superior to the traditional RBF neural network in error and accuracy.

Section: Establishment Of Physical Education Teaching Effect Evaluation System Based On Rbfnn-pso Systemmentioning

confidence: 99%

Evaluation Model of Physical Education Effect: On the Application of Radial Basis Function-Particle Swarm Optimization Neural Network (RBFNN-PSO)

Chen

2021

“…Coaches then predict the results of sports competition according to the students' or athletes' usual training situation, test results, compare and analyze the actual results, so as to find out the weak points of training, and then determine the next step of training content and plan [3,4]. is kind of sports performance prediction can not only help coaches to make an accurate and reasonable judgment on the sports state of athletes or students but also provide a clear direction for future training so that coaches can make more reasonable competition goals.…”

Section: Introductionmentioning

confidence: 99%

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

Jin-juan

2021

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.

“…In conclusion, it can be seen that in the process of design and ultimate load treatment of the bridge prestress mechanical performance analysis model under the current fatigue load, there are problems with high data redundancy, low calculation efficiency, and poor ultimate load effect [17]. And most of them did not involve intelligent algorithms combined with distributed computing technology and did not apply intelligent solutions to the study of prestress loss of bridges [18]. On the other hand, although a lot of basic research has been done on the design and mechanical performance analysis of the bridge prestress under the fatigue load, the research results on the mechanical performance analysis and bearing capacity design of the bridge under different types of fatigue load are relatively few, and there is no strong robust model for limit load effect analysis [19].…”

Section: Introductionmentioning

confidence: 99%

Determination of Bridge Prestress Loss under Fatigue Load Based on PSO-BP Neural Network

Wang

2021

During the service period of a prestressed concrete bridge, as the number of cyclic loads increases, cumulative fatigue damage and prestress loss will occur inside the structure, which will affect the safety, durability, and service life of the structure. Based on this, this paper studies the loss of bridge prestress under fatigue load. First, the relationship between the prestress loss of the prestressed tendons and the residual deflection of the test beam is analyzed. Based on the test results and the main influencing factors of fatigue and creep, a concrete fatigue and creep calculation model is proposed; then, based on the static cracking check calculation method and POS-BP neural network algorithm, a prestressed concrete beam fatigue cracking check model under repeated loads is proposed. Finally, the mechanical performance of the prestressed concrete beam after fatigue loading is analyzed, and the influence of the fatigue load on the bearing capacity of the prestressed concrete beam is explored. The results show that the bridge prestress loss characterization model based on the POS-BP neural network algorithm has the advantages of high calculation efficiency and strong applicability.