scholarly journals Application of Neural Network Algorithm Combined with Bee Colony Algorithm in English Course Recommendation

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Guiting Ren
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Convergence Speed ◽  
Bp Network ◽  
English Curriculum ◽  
Bee Colony ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Solution Accuracy

The traditional BP neural network has the disadvantages of easy falling into local minimum and slow convergence speed. Aiming at the shortcomings of BP neural network (BP neural network), an artificial bee colony algorithm (ABC) is proposed to cross-optimize the weight and threshold of BP network parameters. This study is mainly about the application of BP neural network algorithm in English curriculum recommendation technology. It includes the application of BP neural network algorithm in English course recommendation technology, English course teaching design mode, the application of BP neural network algorithm in English course, and the optimal combination of bee colony algorithm and BP neural network. After 4690 iterations, the neural network reaches the target accuracy, and the training is completed. At the same time, the prediction error of the model is less than 10%, which further shows that the performance of the prediction model is good. Therefore, the combination model is recommended in this paper. The results show that the optimization algorithm improves the solution accuracy and speeds up the convergence speed of the 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.

Study on Data Security Technology of Neural Network System

2014 ◽  
Vol 701-702 ◽  
pp. 1041-1044
Author(s):  
Yan Wei Hong
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Security Technology ◽  
Fusion Algorithm ◽  
Basic Principles ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network System ◽  
Neural Network Algorithm ◽  
And Training

This paper analyzes the neural network algorithm model, introduces the basic principles and training process of BP neural network algorithm, analyzes the BP neural network weights adjustment processand the method of determining the number of nodes in each layer; in improved protocol algorithm basis LEACH-E, combined with the BP neural network algorithm, we propose a new data fusion algorithm BPDFA to reduce energy consumption to attain the network lifetime goal.

Study on PCA-Based BP Neural Network Algorithm

2014 ◽  
Vol 530-531 ◽  
pp. 517-521
Author(s):  
Jian Qing Hong ◽  
De'an Zhao ◽  
Wei Kuan Jia
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Computing Time ◽  
Principal Component ◽  
Bp Algorithm ◽  
Complex Data ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Low Dimensional

Using the neural network to deal with complex data, because the pending sample with many variables, aiming at this nature of the pending sample and the structure properties of the BP neural network, in this paper, we propose the new BP neural network algorithm base on principal component analysis (PCA-BP algorithm). The new algorithm through PCA dimension reduction for complex data, got the low-dimensional data as the BP neural networks input, it will be beneficial to design the hidden layer of neural network, save a lot of storage space and computing time, and conductive to the convergence of the neural network. In order to verify the validity of the new algorithm, compared with the traditional BP algorithm, through the case analysis, the result show that the new algorithm improve the efficiency and recognition precise, worthy of further promotion.

Learning perception prediction and English hierarchical model based on neural network algorithm

2020 ◽  
pp. 1-12
Author(s):  
Zhang Wenjuan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Network Performance ◽  
Large Data ◽  
Complex Data ◽  
Examination System ◽  
Model Based ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm

The traditional English examination and the current examination system have been unable to meet the needs of the education industry for English examinations. In view of this, based on the neural network algorithm, this study proposes a hierarchical network management model from the user’s perspective. Based on the in-depth study of the neural network, this study combined with the network performance characteristics of large data volume, complex data to propose a new BP neural network algorithm. By dynamically changing the momentum factor and learning rate, the algorithm has greatly improved the accuracy and stability of the error. In addition, this study proposes a user perception prediction model, and the model is continuously trained on the model based on the improved BP neural network algorithm and the monitored network performance. In order to study the performance of the research model, a control experiment is designed to analyze the performance of the model. The research results show that the intelligent model and algorithm proposed in this paper are completely feasible and effective.

scholarly journals An Algorithm for Solving Robot Inverse Kinematics Based on FOA Optimized BP Neural Network

2021 ◽  
Vol 11 (15) ◽  
pp. 7129
Author(s):  
Yonghua Bai ◽  
Minzhou Luo ◽  
Fenglin Pang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Inverse Kinematics ◽  
Convergence Speed ◽  
Robot Kinematics ◽  
Algebraic Solution ◽  
Slow Convergence ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Control Accuracy

The solution of robot inverse kinematics has a direct impact on the control accuracy of the robot. Conventional inverse kinematics solution methods, such as numerical solution, algebraic solution, and geometric solution, have insufficient solution speed and solution accuracy, and the solution process is complicated. Due to the mapping ability of the neural network, the use of neural networks to solve robot inverse kinematics problems has attracted widespread attention. However, it has slow convergence speed and low accuracy. This paper proposes the FOA optimized BP neural network algorithm to solve inverse kinematics. It overcomes the shortcomings of low convergence accuracy, slow convergence speed, and easy to fall into local minima when using BP neural network to solve inverse kinematics. The experimental results show that using the trained FOA optimized BP neural network to solve the inverse kinematics, the maximum error range of the output joint angle is [−0.04686, 0.1271]. The output error of the FOA optimized BP neural network algorithm is smaller than that of the ordinary BP neural network algorithm and the PSO optimized BP neural network algorithm. Using the FOA optimized BP neural network algorithm to solve the robot kinematics can improve the control accuracy of the robot.

scholarly journals APPLICATION OF BACK PROPAGATION NEURAL NETWORK IN SPORTS FATIGUE INDICATORS

2021 ◽  
Vol 27 (3) ◽  
pp. 249-252
Author(s):  
Xiaoli Wang ◽  
Chunmin Dai
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Principal Component ◽  
Level Of Evidence ◽  
Bp Network ◽  
Training Time ◽  
Emg Signal ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Learning Machine

ABSTRACT Introduction High-intensity rehabilitation training will produce exercise fatigue. Objective A backpropagation (BP) network neural algorithm is proposed to predict sports fatigue based on electromyography (EMG) signal images. Methods The principal component analysis algorithm is used to reduce the dimension of EMG signal features. The knee joint angle is estimated by the regularized over-limit learning machine algorithm and the BP neural network algorithm. Results The RMSE value of the regularized over-limit learning machine algorithm is lower than that of the BP neural network algorithm. At the same time, the ρ value of the regularized over-limit learning machine algorithm is closer to 1, indicating its higher accuracy. Conclusions The model training time of the regularized over-limit learning machine algorithm has been greatly reduced, which improves efficiency. Level of evidence II; Therapeutic studies - investigation of treatment results.

Using the Improved Genetic BP Neural Network Algorithm for Lithologic Identification

2014 ◽  
Vol 1037 ◽  
pp. 389-392 ◽  
Author(s):  
Shui Ming He ◽  
Lu Wen Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Convergence Speed ◽  
Experimental Results ◽  
Crossover Operator ◽  
Generalization Performance ◽  
Algorithm Convergence ◽  
Network Algorithm ◽  
Neural Network Algorithm

in order to fully optimize the BP neural network, and make it have better generalization performance, we improve and design a genetic BP neural network, the algorithm is given, and their crossover operator was improved. And this method is applied in identifying lithology, the experimental results show that this method increases the algorithm convergence speed and effect, and it has certain practical value.

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