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.

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.

Prediction of Sand Thickness Using Seismic Attributes Based on BP-Neural Network

2012 ◽  
Vol 524-527 ◽  
pp. 180-183
Author(s):  
Feng Gao
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Seismic Attributes ◽  
Bp Network ◽  
Sand Body ◽  
Precise Prediction ◽  
Energy Maximum ◽  
Bp Model ◽  
Rms Amplitude

Total energy, maximum peak amplitude and RMS amplitude are sensitive to sand body, and they are non-linear relations with sand thickness. In this study, a three-layer BP neural network is employed to build the prediction model. Nine samples were analyzed by three-layer BP network. The relationships were produced by BP network between sand thickness and the three seismic attributes. The precise prediction results indicate that the three-layer BP network based modeling is a practically very useful tool in prediction sand thickness. The BP model provided better accuracy in prediction than other methods.

scholarly journals Prediction of Lung Infection during Palliative Chemotherapy of Lung Cancer Based on Artificial Neural Network

2022 ◽  
Vol 2022 ◽  
pp. 1-7
Author(s):  
Wei Guo ◽  
Guoyun Gao ◽  
Jun Dai ◽  
Qiming Sun
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Cancer Patients ◽  
Network Model ◽  
Neural Network Model ◽  
Palliative Chemotherapy ◽  
Lung Infection ◽  
Lung Cancer Patients

Lung infection seriously affects the effect of chemotherapy in patients with lung cancer and increases pain. The study is aimed at establishing the prediction model of infection in patients with lung cancer during chemotherapy by an artificial neural network (ANN). Based on the data of historical cases in our hospital, the variables were screened, and the prediction model was established. A logistic regression (LR) model was used to screen the data. The indexes with statistical significance were selected, and the LR model and back propagation neural network model were established. A total of 80 cases of advanced lung cancer patients with palliative chemotherapy were predicted, and the prediction performance of different model was evaluated by the receiver operating characteristic curve (ROC). It was found that age ≧ 60 years, length of stay ≧ 14  d, surgery history, combined chemotherapy, myelosuppression, diabetes, and hormone application were risk factors of infection in lung cancer patients during chemotherapy. The area under the ROC curve of the LR model for prediction lung infection was 0.729 ± 0.084 , which was less than that of the ANN model ( 0.897 ± 0.045 ). The results concluded that the neural network model is better than the LR model in predicting lung infection of lung cancer patients during chemotherapy.

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.

Railway Freight Turnover Forecast Based on the BP Neural Network

2014 ◽  
Vol 536-537 ◽  
pp. 837-840
Author(s):  
Jiang Sun ◽  
Chong Wei
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Railway Freight ◽  
Bp Neural Network Model ◽  
Trained Network

A BP neural network model was employed to forecast the railway freight turnover. First, this paper analyses the data of railway freight turnover in China from 1998 to 2012, build a three layers BP neural network, then by training and learning, a well-trained network can be used for simulating and forecasting. Finally, predict by the Grey GM(1,1) model and well-trained BP neural network respectively, and compares the errors of two prediction model, the results show that predicting the railway freight turnover by BP neural network has higher precision.

Prediction on China's Energy Demand Based on RBF Neural Network Model

2013 ◽  
Vol 805-806 ◽  
pp. 1421-1424
Author(s):  
Xue Feng ◽  
Wuyunbilige Bao ◽  
Ben Ha
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Neural Network Model ◽  
Energy Demand ◽  
Rbf Neural Network ◽  
Bp Network ◽  
Factors Affecting ◽  
Rbf Network ◽  
Network Prediction ◽  
Main Factors

Choose factors which influence the energy demand by the method of path analysis, build radial basis function (RBF) neural network model to predict energy demand in China. The RBF neural network is trained with the actual data of the main factors affecting energy demand during 1989-2003 and energy demand during 1993-2007 as learning sample with a good fitting effect. After testing network with the actual data of the main factors affecting energy demand during 2004-2007 and energy demand during 2008-2011, higher prediction accuracy can be obtained. By comparison with the BP network, RBF network prediction model outperforms BP network prediction model, finally RBF network is applied to make prediction of energy consumption for the year 2013-2015.

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.

A Model Based on BP Neural Network for Audible Noise Prediction

2014 ◽  
Vol 986-987 ◽  
pp. 1356-1359
Author(s):  
You Xian Peng ◽  
Bo Tang ◽  
Hong Ying Cao ◽  
Bin Chen ◽  
Yu Li
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Transmission Lines ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Absolute Error ◽  
Research Area ◽  
Noise Prediction ◽  
Bp Network ◽  
Model Based

Audible noise prediction is a hot research area in power transmission engineering in recent years, especially come down to AC transmission lines. The conventional prediction models at present have got some problems such as big errors. In this paper, a prediction model is established based on BP network, in which the input variables are the four factors in the international common expression of power line audible noise and the noise value is the output. Take multiple measured power lines as an example, a train is made by the BP network and then the prediction model is set up in the hidden layer of the network. Using the trained model, the audible noise values are predicted. The final results show that the average absolute error in absolute terms of the values by the audible noise prediction model based on BP neural network is 1.6414 less than that predicted by the GE formula.

ANALYSIS AND MICROHARDNESS PROFILE OF HOT DIPPING COATING ON LOW-ALLOY STEEL

2020 ◽  
pp. 2050059
Author(s):  
ABEER SHEHAB ◽  
ATHIL AL-EZZI ◽  
ALI AL KATAWY
Keyword(s):  
Alloy Steel ◽  
Steel Surface ◽  
Zinc Coating ◽  
Hardness Profile ◽  
Low Alloy Steel ◽  
Zinc Bath ◽  
Pure Zinc ◽  
Microhardness Profile ◽  
Steel Aisi ◽  
Dipping Process

This research is an effort to understand the morphology of the coating produced during hot dipping process in pure zinc bath based on iron–zinc phase diagram. In this investigation, zinc coating on low-alloy steel AISI 4340 samples was applied by hot dipping method followed by an annealing process. Morphological characterizations of the steel surface layer were accomplished by optical microscopy (OM) and scanning electron microscopy (SEM) equipped with energy-dispersive X-ray spectrometry (EDS). Results revealed that the coating is bonded to the steel surface through a sequence of Fe–Zn layers namely alpha ([Formula: see text], delta ([Formula: see text] and zeta ([Formula: see text] with uniform sloping hardness profile.

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