The BP Network Model for the Control of the Coagulation and the Filter Backwashing System

2008 ◽  
Author(s):  
Xuenong Yi ◽  
QiuMei Wei ◽  
ChenGuang Li
Keyword(s):  
Network Model ◽  
Bp Network ◽  
Filter Backwashing

A Novel Quantum Neural Network Model with Variable Selection for Short Term Load Forecasting

2010 ◽  
Vol 20-23 ◽  
pp. 612-617 ◽  
Author(s):  
Wei Sun ◽  
Yu Jun He ◽  
Ming Meng
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Load Forecasting ◽  
Short Term ◽  
Bp Network ◽  
Quantum Neural Network ◽  
Short Term Load Forecasting ◽  
Selection For

The paper presents a novel quantum neural network (QNN) model with variable selection for short term load forecasting. In the proposed QNN model, first, the combiniation of maximum conditonal entropy theory and principal component analysis method is used to select main influential factors with maximum correlation degree to power load index, thus getting effective input variables set. Then the quantum neural network forecating model is constructed. The proposed QNN forecastig model is tested for certain province load data. The experiments and the performance with QNN neural network model are given, and the results showed the method could provide a satisfactory improvement of the forecasting accuracy compared with traditional BP network model.

scholarly journals Research on Deformation Prediction of Foundation Pit Based on PSO-GM-BP Model

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Dongge Cui ◽  
Chuanqu Zhu ◽  
Qingfeng Li ◽  
Qiyun Huang ◽  
Qi Luo
Keyword(s):  
Network Model ◽  
Network Models ◽  
Particle Swarm Algorithm ◽  
Neural Network Models ◽  
Foundation Pit ◽  
Bp Network ◽  
Deformation Prediction ◽  
Application Range ◽  
Limited Applicability ◽  
Bp Model

Deformation prediction is significant to the safety of foundation pits. Against with low accuracy and limited applicability of a single model in forecasting, a PSO-GM-BP model was established, which used the PSO optimization algorithm to optimize and improve the GM (1, 1) model and the BP network model, respectively. Combining a small amount of measured data during the excavation of a bottomless foundation pit in a Changsha subway station, the calculations based on the PSO-GM model, the PSO-BP network model, and the PSO-GM-BP model compared. The results show that both the GM (1, 1) and BP neural network models can predict accurate results. The prediction optimized by the particle swarm algorithm is more accurate and has more substantial applicability. Due to its reliable accuracy and wide application range, the PSO-GM-BP model can effectively guide the construction of foundation pits, and it also has certain reference significance for other engineering applications.

Research of the System Based on BP Network for the Prediction of Grape Disease

2013 ◽  
Vol 462-463 ◽  
pp. 476-480
Author(s):  
Feng Bao ◽  
Juan Wang ◽  
Zhen Hui Ren
Keyword(s):  
Influencing Factors ◽  
Network Model ◽  
Network Structure ◽  
Good Effect ◽  
Bp Network ◽  
Disease Occurrence ◽  
Sample Data ◽  
Long Term Prediction ◽  
Selection Of

The text introduced a system based on BP network for the prediction of grape disease. It included the design of a network structure, the selection of parameter for network study, the processing of sample data etc. With the use of BP network model, this system can forecast the extent of grape disease, so it is applicable to the conditions which have many influencing factors, complicated relationship, difficulty of analyze quantitatively and requirement of long-term prediction. Using this system to the prediction of grape disease in Zhuo Lu area Zhang Jia Kou city, the authors obtained a good effect, which is of value to the prediction of grape disease occurrence.

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.

Prediction Model of Zinc Coating Weight for Continuous Hot-Dip Galvanizing Based on BP Network

2010 ◽  
Vol 145 ◽  
pp. 10-13
Author(s):  
Xi Bang Zhang ◽  
Qing Dong Zhang ◽  
Xiao Feng Zhang
Keyword(s):  
Prediction Model ◽  
Network Model ◽  
Zinc Coating ◽  
Simulation Function ◽  
Zinc Bath ◽  
Bp Network ◽  
Coating Weight ◽  
Pressure Nozzle

The BP network model is established to predict the Zinc Coating Weight for C608/708 hot-dip galvanizing line. The model develops as the simulation function of jet pressure, nozzle to strip distance, nozzle to zinc bath distance, strip velocity and thickness. Compare the prediction precision of different models when the number of neurons in network is five, ten or fifteen. The models have better effect in use after put into production and can be used for other hot-dip galvanizing lines.

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.

To Optimize a BP Network System

2011 ◽  
Vol 50-51 ◽  
pp. 919-923
Author(s):  
Wei Tong ◽  
Li Ping Qin
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Credit Risk ◽  
Network Model ◽  
Neural Network Model ◽  
Network System ◽  
Data Set ◽  
Bp Network ◽  
Credit Risk Assessment ◽  
The Neural Network

The neural network has been introduced into the studies of credit risk assessment. However, the ratio of the dataset for training and testing is difficult to determine, so the neural network is not robust enough to give the judgment. Therefore, using the 2000 instances of personal consumer credit data set for approval of credit applications of a provincial-level China Construction Bank, for the BP neural network model, the study focused on the ratio of the dataset for training and testing. The results show that, when the ratio of the dataset for training and testing is 800:1200, the neural network model 2 for credit risk assessment has better performance. And it can achieve the desired accuracy and computational efficiency, so the BP network system for credit risk assessment is optimized.

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