BP neural network predictive model for stray current density of a buried metallic pipeline

2010 ◽  
Vol 57 (5) ◽  
pp. 234-237 ◽  
Author(s):  
A. Lin Cao ◽  
Qing Jun Zhu ◽  
Sheng Tao Zhang ◽  
Bao Rong Hou
Keyword(s):  
Neural Network ◽  
Predictive Model ◽  
Current Density ◽  
Bp Neural Network ◽  
Stray Current

scholarly journals An Air Quality Predictive Model of Licang of Qingdao City Based on BP Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 3366-3371 ◽  
Author(s):  
Ruo Bo Xin ◽  
Zhi Fang Jiang ◽  
Ning Li ◽  
Lu Jian Hou
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Predictive Model ◽  
Bp Neural Network ◽  
Ambient Air ◽  
Monitoring Site ◽  
Ambient Air Quality ◽  
Bp Network ◽  
Air Quality Forecast ◽  
Qingdao City

In order to obtain high precision results of urban air quality forecast, we propose a short-term predictive model of air quality in this paper, which is on the basis of the ambient air quality monitoring data and relevant meteorological data of a monitoring site in Licang district of Qingdao city in recent three years. The predictive model is based on BP neural network and used to predict the ambient air quality in the next some day or within a certain period of hours. In the design of the predictive model, we apply LM algorithm, Simulated Annealing algorithm and Early Stopping algorithm into BP network, and use a reasonable method to extract the historical data of two years as the training samples, which are the main reasons why the prediction results are better both in speed and in accuracy. And when predicting within a certain period of hours, we also adopt an average and equivalent idea to reduce the error accuracy, which brings us good results.

Research on Temperature Field of Milling Insert with 3D Complex Groove

2007 ◽  
Vol 10-12 ◽  
pp. 374-378
Author(s):  
Ming Yang Wu ◽  
Q.X. Meng ◽  
Qiang Liu
Keyword(s):  
Neural Network ◽  
Temperature Field ◽  
Steady State ◽  
Predictive Model ◽  
Bp Neural Network ◽  
Local Minimum ◽  
State Characteristic ◽  
Key Technology ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt

Prediction of temperature field is a key technology to achieve the groove design and reconstruction of milling insert, predictive model of neural network is a new way to achieve the prediction of temperature field. According to the non-steady state characteristic of temperature field of milling insert, the paper puts forward a predictive model of temperature field of milling insert with 3D complex groove based on Levenberg-Marquardt algorithm of BP neural network, and it overcomes the disadvantage that traditional neural network is easy to fall into local minimum. The predictive results show that this predictive model can converge quickly and predict accurately.

Predictive Model Based on Genetic Algorithm-Neural Network for Fatigue Performances of Pre-Corroded Aluminum Alloys

2007 ◽  
Vol 353-358 ◽  
pp. 1029-1032 ◽  
Author(s):  
Chao Hua Fan ◽  
Yu Ting He ◽  
Heng Xi Zhang ◽  
Hong Peng Li ◽  
Feng Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Aluminum Alloy ◽  
Aluminum Alloys ◽  
Predictive Model ◽  
Bp Neural Network ◽  
Neural Network Method ◽  
Network Algorithm ◽  
Network Method ◽  
Neural Network Algorithm

In the paper, genetic algorithm is introduced in the study of network authority values of BP neural network, and a GA-NN algorithm is established. Based on this genetic algorithm-neural network method, a predictive model for fatigue performances of the pre-corroded aluminum alloys under a varied corrosion environmental spectrum was developed by means of training from the testing dada, and the fatigue performances of pre-corroded aluminum alloys can be predicted. The results indicate that genetic algorithm-neural network algorithm can be employed to predict the underlying fatigue performances of the pre-corroded aluminum alloy precisely, compared with traditional neural network.

APPLICATION OF BACK-PROPAGATION NEURAL NETWORKS FOR CORROSION BEHAVIOR ESTIMATION OF Ni-TiN COATINGS FABRICATED THROUGH PULSE ELECTRODEPOSITION

2019 ◽  
Vol 26 (03) ◽  
pp. 1850154
Author(s):  
DONGJIE GUO ◽  
YUBING HAN ◽  
CHUNYANG MA ◽  
WANYING YU ◽  
FAFENG XIA ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Network Model ◽  
Current Density ◽  
Bp Neural Network ◽  
Particle Concentration ◽  
Back Propagation ◽  
Pulse Frequency ◽  
Weight Losses ◽  
Tin Coatings ◽  
Bp Neural Network Model

In this paper, back-propagation (BP) neural network model with 8 hidden layers and 10 neurons was utilized to estimate corrosion behaviors of Ni-TiN coatings, deposited through pulse electrodeposition. Effects of plating parameters, namely, pulse frequency, TiN concentration and current density, on Ni-TiN coatings weight losses were discussed. Results indicated that pulse frequency, TiN concentration and current density had significant effects on weight losses of Ni-TiN coatings. Maximum mean square error of BP model was 9.10%, and this verified that the BP neural network model could accurately estimate corrosion behavior of Ni-TiN coatings. The coating fabricated at pulse frequency of 500[Formula: see text]Hz, TiN particle concentration of 8[Formula: see text]g/L and current density of 4[Formula: see text]A/dm2 consisted of fine grains and compact oxides, demonstrating that the coating displayed best corrosion resistance in this corrosion test. Concentrations of Ti and Ni in Ni-TiN coating prepared at pulse frequency of 500[Formula: see text]Hz, TiN particle concentration of 8[Formula: see text]g/L and current density of 4[Formula: see text]A/dm2 were 18.6[Formula: see text]at.% and 55.4[Formula: see text]at.%, respectively.

BP Neural Network Model in the Application of Subway Drainage Cabinet

2013 ◽  
Vol 380-384 ◽  
pp. 2601-2604
Author(s):  
Li Li Liu ◽  
Wen Si
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Stray Current ◽  
Prediction Mode ◽  
Subway System ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Simulation Results ◽  
Bp Neural Network Model ◽  
Bp Model

In view of the changeable characteristic of the stray current in subway system, introduced the artificial neural network BP model, using Matlab prepared a program of the BP neural network prediction mode to predict the current drainage cabinet exhaust flow .The simulation results show that the prediction model is simple and efficient to the automatic drainage cabinet drainage strategy in further promote.

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