scholarly journals The Application and Research of the GA-BP Neural Network Algorithm in the MBR Membrane Fouling

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Chunqing Li ◽  
Zixiang Yang ◽  
Hongying Yan ◽  
Tao Wang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Bp Neural Network ◽  
Membrane Fouling ◽  
Sewage Treatment ◽  
Principal Component ◽  
Operating Pressure ◽  
Bp Network ◽  
Factors Affecting ◽  
Membrane Flux

It is one of the important issues in the field of today's sewage treatment of researching the MBR membrane flux prediction for membrane fouling. Firstly this paper used the principal component analysis method to achieve dimensionality and correlation of input variables and obtained the three major factors affecting membrane fouling most obvious: MLSS, total resistance, and operating pressure. Then it used the BP neural network to establish the system model of the MBR intelligent simulation, the relationship between three parameters, and membrane flux characterization of the degree of membrane fouling, because the BP neural network has slow training speed, is sensitive to the initial weights and the threshold, is easy to fall into local minimum points, and so on. So this paper used genetic algorithm to optimize the initial weights and the threshold of BP neural network and established the membrane fouling prediction model based on GA-BP network. As this research had shown, under the same conditions, the BP network model optimized by GA of MBR membrane fouling is better than that not optimized for prediction effect of membrane flux. It demonstrates that the GA-BP network model of MBR membrane fouling is more suitable for simulation of MBR membrane fouling process, comparing with the BP network.

scholarly journals Prediction Model of Iron Ore Pellet Ambient Strength and Sensitivity Analysis on the Influence Factors

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 593 ◽  
Author(s):  
Qiangjian Gao ◽  
Yingyi Zhang ◽  
Xin Jiang ◽  
Haiyan Zheng ◽  
Fengman Shen
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Influence Factors ◽  
Principal Component ◽  
Bp Network ◽  
Input Variables ◽  
Artificial Neural Network Ann

The Ambient Compressive Strength (CS) of pellets, influenced by several factors, is regarded as a criterion to assess pellets during metallurgical processes. A prediction model based on Artificial Neural Network (ANN) was proposed in order to provide a reliable and economic control strategy for CS in pellet production and to forecast and control pellet CS. The dimensionality of 19 influence factors of CS was considered and reduced by Principal Component Analysis (PCA). The PCA variables were then used as the input variables for the Back Propagation (BP) neural network, which was upgraded by Genetic Algorithm (GA), with CS as the output variable. After training and testing with production data, the PCA-GA-BP neural network was established. Additionally, the sensitivity analysis of input variables was calculated to obtain a detailed influence on pellet CS. It has been found that prediction accuracy of the PCA-GA-BP network mentioned here is 96.4%, indicating that the ANN network is effective to predict CS in the pelletizing process.

scholarly journals Evaluation model of art internal auxiliary teaching quality based on artificial intelligence under the influence of COVID-19

2020 ◽  
Vol 39 (6) ◽  
pp. 8713-8721
Author(s):  
Luo Yuan ◽  
Zhao Xiaofei ◽  
Qiu Yiyu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Bp Neural Network ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Teaching Quality ◽  
Empirical Method ◽  
Bp Network ◽  
Art Teaching ◽  
The Impact

At present, the evaluation of normal teaching order and teaching quality has been seriously interfered by the impact of COVID-19. In order to ensure the quality of art classroom teaching, this article uses BP neural network technology to build a model for art teaching quality evaluation during the epidemic. Based on the introduction of the BP neural network model and the problems of art teaching quality evaluation, the article focuses on the art teaching quality evaluation indicators and the BP neural network algorithm and process. In addition, the article also uses an empirical method to verify the effect of the BP network model training method, and obtains the expected effect. Finally, it discusses the problem of information processing in art teaching evaluation.

Method of AC Servo System Based on Genetic Algorithm and Neural Network PID Control

2013 ◽  
Vol 717 ◽  
pp. 563-567 ◽  
Author(s):  
Wen Chun Chang ◽  
Cheng Chen
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Network Structure ◽  
Bp Neural Network ◽  
Extreme Values ◽  
Learning Algorithm ◽  
Bp Algorithm ◽  
Bp Network ◽  
Neural Network Learning

BP network model has become one of the important neural network model, is used in many fields, but it has some defects. As from a mathematical perspective, it is a nonlinear optimization problem, which inevitably has the local minima problem; BP neural network learning algorithm has slow convergence rate, and the convergence speed and the initial weights of choice; network structure, namely the hidden layer nodes selection is still no theory until, but according to the experience. Based on the BP algorithm the local extreme values, considering the genetic algorithm and BP algorithm is combined with, on the BP neural network optimization. Neural network using genetic algorithm optimization mainly includes three aspects: the connection weights of evolution, evolutionary network structure, learning the rules of evolution.

The Forecast of Co-Rotating Twin-Screw Extruder's Screw Based on BP Neural Network Model

2014 ◽  
Vol 711 ◽  
pp. 257-260
Author(s):  
Guang Sheng Zeng ◽  
Hui Zhang ◽  
Hua Chen
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Simple Method ◽  
Bp Network ◽  
Plastic Composite ◽  
Target Yield ◽  
Twin Screw ◽  
Bp Neural Network Model

The established BP neural network model can forecast one of the wood-plastic composite molding equipment the screw of extruder with co-rotating twin-screw has been presented. The content of fiber and lubricant, the appropriate temperature of the extruder and the target yield are determined as the variable inputs, while the outputs are the diameters of the screw stem. This intelligent BP network model can forecast the condition of actual production precisely by setting the input properties of the material and the integration of products in order to fit the requirements of the wood-plastic composited production more successfully. Through the analysis of BP neural network model, the former simple method of choosing the screw stem’ diameters only depend on yield can be modified more efficiently.

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.

scholarly journals Application of machine learning algorithms in MBR simulation under big data platform

2020 ◽  
Vol 15 (4) ◽  
pp. 1238-1247
Author(s):  
Weiwei Li ◽  
Chunqing Li ◽  
Tao Wang
Keyword(s):  
Big Data ◽  
Random Forest ◽  
Membrane Fouling ◽  
Sewage Treatment ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Random Forest Algorithm ◽  
Membrane Flux ◽  
Data Platform

Abstract Membrane bioreactors (MBRs) are a sewage treatment process that combines membrane separation with bioreactor technology. It has great advantages in sewage treatment. Membrane fouling hinders MBR process development, however. Studies have shown that the degree of membrane fouling can be judged using the membrane flux rate. In this study, principal component analysis was used to extract the main factors affecting membrane fouling, then the random forest algorithm on the Hadoop big data platform was used to establish an MBR membrane flux prediction model, which was tested. In order to verify the model's effectiveness, BP neural network and SVM support vector machine models were established using the same experimental data. The experimental results from the different models were compared, and the results showed that the random forest algorithm gave the best MBR membrane flux predictions.

scholarly journals Prediction of Rubber Aging Life by Intelligent Neural Network

2018 ◽  
Vol 175 ◽  
pp. 03070
Author(s):  
HE Hong ◽  
HU WenQi ◽  
GAO ShenJun
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Optimization Model ◽  
Optimal Solution ◽  
Algorithm Optimization ◽  
Bp Network ◽  
Stability And Accuracy ◽  
Bp Neural Network Model

Predicting the agin life of rubber is mostly based on traditional dynamic methods. These methods often have some limitations, which can not reflect the influence of certain factors. To avoid such limitations, a BP neural network model was established to predict the aging life of rubber. Comparing with the BP neural network model, results from the genetic algorithm optimization model (GA-BP) and the particle swarm optimization model (PSO-BP) showed that the GA-BP network model has better stability and accuracy and can quickly get the global optimal solution. The prediction accuracy of the GA-BP neural network model is better than that of the traditional dynamic model and its result is in good agreement with the experimental data.

scholarly journals BP Neural Network Model-Based Physical Exercises and Dietary Habits Relationships Exploration

2015 ◽  
Vol 9 (1) ◽  
pp. 276-281 ◽  
Author(s):  
Xingwei Guo ◽  
Xuesheng Zhang ◽  
Yi Sun
Keyword(s):  
Neural Network ◽  
Physical Exercise ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Dietary Habits ◽  
Principal Component ◽  
Exercise Time ◽  
Physical Exercises ◽  
Bp Neural Network Model

With the continuous progress of society and increment of social pressure, people have paid little and little attentions to physical exercises and dietary necessity. Taking Beijing, Shanghai, Guangzhou, Shenzhen, Shijiazhuang and Baotou university students as research objects, targeted at physical exercises time and dietary habits, it starts investigation. Making principal component analysis of investigation results, it indicates that cereal intake is principal component in dietary habits; strenuous exercise time and general physical exercise time are the principal components in physical exercise. Utilizing BP neural network model, and analyzing these seven cities’ physical exercises and dietary habits conditions, the result indicates that except for Shenzhen, all the other six cities haven’t reached the standard.

scholarly journals Prediction of Shear Characteristics of Unsaturated Soil Based on BP Neural Network

2020 ◽  
Vol 165 ◽  
pp. 03034
Author(s):  
Jia Lei Liang ◽  
Guang Ri Jin ◽  
Zhi Xie Shen
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Unsaturated Soil ◽  
Bp Neural Network ◽  
Layer Structure ◽  
Network Models ◽  
Shear Strength Parameters ◽  
Bp Network ◽  
Shear Characteristics ◽  
Hidden Layer

The prediction model of shear strength parameters of unsaturated soil based on indoor test data is established by using BP neural network. Five kinds of network models with different number of hidden layer nodes are trained and studied, and the best network model is selected to conduct the prediction. The results show that the optimal BP network model is a single hidden layer structure of 8-16-2. Using this model to predict, the correlation coefficient and regression coefficient between the predicted value and the measured value are high, and the predicted result is reliable, so the method has certain practicability.

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