scholarly journals Signal Recognition Based on APSO-RBF Neural Network to Assist Athlete’s Competitive Ability Evaluation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Feng Guo ◽  
Qingcheng Huang
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Particle Swarm Optimization ◽  
Competitive Ability ◽  
Rbf Neural Network ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Technical Ability ◽  
Real Value

The advanced analysis and research methods of big data will provide theoretical support for the integration of athletes’ talent training. The advanced technological methods of big data will also give full play to the advantages of tapping the potential of talents and actively improve the success rate of grassroots young athletes. This paper proposes an improved Adaptive Particle Swarm Optimization (APSO) algorithm for the optimization of radial basis function (RBF) neural network parameters. The basic structure of RBF neural network is introduced, and the influence of parameters on the performance of RBF neural network is analyzed. The optimization method of RBF neural network parameters is analyzed, and Particle Swarm Optimization (PSO) algorithm is selected as the parameter optimization method of RBF neural network. In addition, an improved APSO algorithm is proposed according to the advantages and disadvantages of PSO and compared with other PSO algorithms. Experimental results show that the improved PSO algorithm has better accuracy. The improved PSO algorithm is applied to the parameter optimization of RBF neural network, and the experimental results prove the superiority of the proposed method. By weighting the second-level indicators, the weights of the second-level indicators of athletes’ competitive ability are in order of skill, athletic quality, psychological ability, and artistic expression. Skills are the main factors that determine the level of competitive ability. Sports quality and psychological ability are important guarantees for supporting the normal performance of skills. Artistic expressiveness is a supplementary factor for competitive ability. The various elements cooperate with each other and interact with each other. The indicators do not exist alone but cooperate with each other to support the formation of the entire athletic ability system. In the content of the competitive ability index of excellent athletes, technical ability is the core, and sports quality, psychological ability, and artistic performance ability ultimately exist to serve the improvement of technical ability. The competition scores of the 100 athletes counted in this article are all above 9.0 points. The difference between APSO-RBF’s action quality scores of 100 athletes and the real value is less than 3%. In terms of movement difficulty, the APSO-RBF evaluated athletes’ scores are close to 1.65 points, which is basically the same as the real value.

scholarly journals Radial Basis Function Neural Network Based on an Improved Exponential Decreasing Inertia Weight-Particle Swarm Optimization Algorithm for AQI Prediction

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jinna Lu ◽  
Hongping Hu ◽  
Yanping Bai
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Radial Basis

This paper proposed a novel radial basis function (RBF) neural network model optimized by exponential decreasing inertia weight particle swarm optimization (EDIW-PSO). Based on the inertia weight decreasing strategy, we propose a new Exponential Decreasing Inertia Weight (EDIW) to improve the PSO algorithm. We use the modified EDIW-PSO algorithm to determine the centers, widths, and connection weights of RBF neural network. To assess the performance of the proposed EDIW-PSO-RBF model, we choose the daily air quality index (AQI) of Xi’an for prediction and obtain improved results.

A Forecasting Model of RBF Neural Network Based on Particle Swarm Optimization

2011 ◽  
Vol 65 ◽  
pp. 605-612
Author(s):  
Yu Min Pan ◽  
Cheng Yu Huang ◽  
Quan Zhu Zhang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Rbf Neural Network ◽  
Particle Swarm ◽  
Optimization Method ◽  
Forecasting Model ◽  
Emission Model ◽  
Gas Emission ◽  
Swarm Optimization ◽  
Random Optimization

In order to improve the precision of gas emission forecasting,this paper proposes a new forecasting model based on Particle Swarm Optimization (PSO).PSO is a novel random optimization method which has extensive capability of global optimization.In the model, PSO is used to optimize the weight,width and center of RBF neural network and the optimal model is applied to forecast gas emission.The diversified factors analysised with grey correlation,MATLAB is employed to implement the model for gas emission forecasting.The simulation results show that the gas emission model optimized by PSO is more accurate than the traditional RBF model．

scholarly journals Position Control of Ultrasonic Motor Using IMC-PID Combined with Tribes Type NN Algorithm

2011 ◽  
Vol 2-3 ◽  
pp. 12-17
Author(s):  
Sheng Lin Mu ◽  
Kanya Tanaka
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Pid Control ◽  
Position Control ◽  
Back Propagation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Ultrasonic Motor ◽  
Swarm Optimization

In this paper, we propose a novel scheme of IMC-PID control combined with a tribes type neural network (NN) for the position control of ultrasonic motor (USM). In this method, the NN controller is employed for tuning the parameter in IMC-PID control. The weights of NN are designed to be updated by the tribes-particle swarm optimization (PSO) algorithm. This method makes it possible to compensate for the characteristic changes and nonlinearity of USM. The parameter-free tribes-PSO requires no information about the USM beforehand; hence its application overcomes the problem of Jacobian estimation in the conventional back propagation (BP) method of NN. The effectiveness of the proposed method is confirmed by experiments.

WNN Model Based on Particle Swarm Optimization for Fault Diagnosis in Analog Circuit

2013 ◽  
Vol 427-429 ◽  
pp. 1048-1051
Author(s):  
Xu Sheng Gan ◽  
Hao Lin Cui ◽  
Ya Rong Wu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Convergence Rate ◽  
Analog Circuit ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Wavelet Neural Network ◽  
Swarm Optimization ◽  
Fast Convergence Rate

In order to diagnose the fault in analog circuit correctly, a Wavelet Neural Network (WNN) method is proposed that uses the Particle Swarm Optimization (PSO) algorithm to optimize the network parameters. For the improvement of convergence rate in WNN based on PSO algorithm, a compressing method in research space is introduced into the traditional PSO algorithm to improve the convergence in WNN training. The simulation shows that the proposed method has a good diagnosis with fast convergence rate for the fault in analog circuit.

scholarly journals Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5609 ◽  
Author(s):  
Shahab S. Band ◽  
Saeid Janizadeh ◽  
Subodh Chandra Pal ◽  
Asish Saha ◽  
Rabin Chakrabortty ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Gully Erosion ◽  
Slope Aspect ◽  
Swarm Optimization ◽  
Proposed Model ◽  
Deep Learning Neural Network

This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70%) and testing (30%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.

A dynamic particle swarm optimization method applied to global optimizations of engineering inverse problem

2018 ◽  
Vol 37 (1) ◽  
pp. 98-117 ◽  
Author(s):  
Shafiullah Khan ◽  
Shiyou Yang ◽  
Obaid Ur Rehman
Keyword(s):  
Inverse Problem ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Content Type ◽  
Novel Strategy ◽  
Modified Pso

Purpose The aim of this paper is to explore the potential of particle swarm optimization (PSO) algorithm to solve an electromagnetic inverse problem. Design/methodology/approach A modified PSO algorithm is designed. Findings The modified PSO algorithm is a more stable, robust and efficient global optimizer for solving the well-known benchmark optimization problems. The new mutation approach preserves the diversity of the population, whereas the proposed dynamic and adaptive parameters maintain a good balance between the exploration and exploitation searches. The numerically experimental results of two case studies demonstrate the merits of the proposed algorithm. Originality/value Some improvements, such as the design of a new global mutation mechanism and introducing a novel strategy for learning and control parameters, are proposed.

New Neural Network Inverse Control of Two-Motor Drive System

2013 ◽  
Vol 416-417 ◽  
pp. 447-453
Author(s):  
Mei Kang ◽  
Wen Xiang Zhao ◽  
Jing Hua Ji ◽  
Guo Hai Liu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Motor System ◽  
Rbf Neural Network ◽  
Particle Swarm ◽  
Drive System ◽  
Motor Drive ◽  
Swarm Optimization ◽  
Synchronous System ◽  
Motor Drive System

Two-motor drive system is a multi-variable, nonlinear and strongly coupled system. A new synchronous control strategy for two-motor system is proposed based on radial basis function (RBF) neural network inverse with particle swarm optimization. To enhance the system performance, the particle swarm optimization is adopted to optimize the RBF nerve center, an optimized RBF neural network inverse and a two-motor system is connected in series to form composite pseudo-linear system. This two-motor synchronous system can be decoupled into two independent linear subsystems for speed and tension. Then, the decoupled control is implemented by designing a linear closed-loop adjustor. The experimental results verify that the two-motor synchronous system can be decoupled well for speed and tension based on the proposed neural network inverse system. Also, the proposed system can deal with external disturbances with strong robustness.

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