scholarly journals Research on Temperature Field Prediction Model of Electric Spindle Based on Improved BP Neural Network

2021 ◽  
Author(s):  
Enwen Zhou ◽  
Yanling Zhao ◽  
Ye Dai ◽  
Jingwei Zhang ◽  
Yuan Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Temperature Field ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Test Method ◽  
Motorized Spindle ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Temperature Field Prediction

Abstract The motorized spindle is the core component of CNC machine tools. In order to ensure its processing performance and processing safety, the temperature field of motorized spindle is studied. The three-dimensional model of the motorized spindle is established, and the convective heat transfer coefficient of the internal heat load and the simulation boundary condition are calculated by combining the heat transfer theory. The simulation is carried out by the finite element analysis software, and the internal temperature distribution of the motorized spindle under thermal steady state is calculated. Based on the numerical simulation analysis method and the thermal balance test method, the data basis for the prediction model of the motorized spindle temperature field is provided. The traditional BP neural network algorithm and PSO-BP neural network algorithm are used to predict the temperature of the motorized spindle measuring point under specific working conditions, and the temperature field prediction results are compared and analyzed. The results show that the PSO-BP neural network prediction model has good compatibility for variable data input, and the prediction results show little difference, which has high prediction accuracy and robustness.

Research on the Prediction Model of Urban Power Grid Short-Term Load Based on BP Neural Network Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 954-957
Author(s):  
Pei Long Xu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Power Grid ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Short Term ◽  
Network Algorithm ◽  
Neural Network Algorithm

Objective: The paper aims to establish the prediction model of urban power grid short-term load based on BP neural network algorithm. Method: Five factors influencing the urban power grid short-term load are used to establish the neural network model: date type, weather, daily maximum temperature, daily minimum temperature and daily average temperature. Matlab toolbox is used to develop the testing platform through VC++ programming. Result: The variable learning rates are 0.35 and 0.64. With 23410 iterations, the model is converged, and the global error is 0.00032. Conclusion: Through the data comparison and analysis, the relative error is within 5%, thus indicating the model is accurate and effective, and it can be used to predict the change of urban power grid short-term load.

scholarly journals The Analysis of Electronic Circuit Fault Diagnosis Based on Neural Network Data Fusion Algorithm

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 458
Author(s):  
Nana Wang
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Compression Test ◽  
Bp Neural Network ◽  
Test Method ◽  
Electronic Circuits ◽  
Pressure Function ◽  
Network Algorithm ◽  
Pressure Test ◽  
Neural Network Algorithm

Symmetries play very important roles in the dynamics of electrical systems. The relevant electronic circuits with fault diagnostics, including the optimized neural network algorithm model, are designed on the basis of symmetry principles. In order to improve the efficiency of the circuit pressure test, a circuit pressure function equivalent compression test method based on the parallel neural network algorithm is proposed. For the implementation stage of the circuit pressure test, the improved modified node algorithm (MNA) is used to build an optimization model, and the circuit network is converted into an ordinary differential equation for the circuit pressure function equivalent compression test. The test aims to minimize flux. Then, backpropagation (BP) neural network algorithm data fusion is introduced to optimize the minimum flux model of the cyclic pressure functional equivalent compression test. Finally, a simulation experiment is carried out to verify the effectiveness of the algorithm in the accuracy and efficiency of the pressure test. The results show that the improved BP neural network improves the data fusion accuracy and shortens the sample training time; compared with the uncompressed algorithm, the running time of the proposed algorithm is greatly reduced and the execution efficiency is high; compared with the vascular pressure test method, there is no significant difference in the convergence accuracy and it is at a level of 10−5. Since the parallel computing problem is not considered in either of the two-pulse tube pressure test methods, the convergence time of the algorithm increases exponentially with the increase in the number of parallel threads. However, the algorithm in this research considers the problem of parallel execution and uses a quad-core processor, with no significant change in computing time and high computing efficiency. Therefore, BP neural network data fusion can be used for the fault diagnosis of electronic circuits, with a high operating efficiency and good development prospects.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jinjuan Wang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Performance Prediction ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Training Model ◽  
Sports Performance ◽  
Advantages And Disadvantages ◽  
Network Algorithm ◽  
Neural Network Algorithm

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.

Precision Power System Harmonic Analysis Algorithm Based on ABC-BPNN

2013 ◽  
Vol 483 ◽  
pp. 630-634
Author(s):  
Shu Chuan Gan ◽  
Ling Tang ◽  
Li Cao ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Power System ◽  
Harmonic Analysis ◽  
Bp Neural Network ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Bee Colony ◽  
Network Algorithm ◽  
Neural Network Algorithm

An algorithm of artificial colony algorithm to optimize the BP neural network algorithm was presented and used to analyze the harmonics of power system. The artificial bee colony algorithm global searching ability, convergence speed for the BP neural network algorithm for harmonic analysis is easy to fall into local optimal solution of the disadvantages, and the initial weights of the artificial bee colony algorithm also greatly enhance whole algorithm model generalization capability. This algorithm using MATLAB for Artificial bee colony algorithm and BP neural network algorithm simulation training toolbox found using artificial bee colony algorithm to optimize BP neural network algorithm converges faster results with greater accuracy, with better harmonic analysis results.

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