Predictive Model of BOF Based on LM-BP Neural Network Combining with Learning Rate

2009 ◽  
Author(s):  
Xiying Ding ◽  
Jian Wang ◽  
Shuping Yang
Keyword(s):  
Neural Network ◽  
Predictive Model ◽  
Bp Neural Network ◽  
Learning Rate

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.

scholarly journals Martial Arts Competitive Decision-Making Algorithm Based on Improved BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Huipeng Lv
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Bp Neural Network ◽  
Martial Arts ◽  
Gradient Descent ◽  
Step Length ◽  
Learning Rate ◽  
Main Body ◽  
Scientific Analysis ◽  
Neural Network Technology

The main body of modern Chinese martial arts competition is the strategy, and fighting has just started in sports competitions. Strategy and action correspond to each other and practice as a set. Therefore, constructing the Chinese martial arts competition decision-making algorithm and perfecting the martial arts competition are intuitive and essential. The formulation of martial arts competition strategies requires scientific analysis of athletic data and more accurate predictions. Based on this observation, this paper combines the popular neural network technology to propose a novel additional momentum-elastic gradient descent. The BP neural network adapts to the learning rate. The algorithm is improved for the traditional BP neural network, such as selecting learning step length, the difficulty of determining the size, and direction of the weight, and the learning rate is not easy to control. The experimental results show that this paper’s algorithm has improved both network scale and running time and can predict martial arts competition routines and formulate scientific strategies.

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.

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

Solution to China’s GDP Prediction Problem by BP Neural Network

2011 ◽  
Vol 50-51 ◽  
pp. 423-427
Author(s):  
Juan Li ◽  
Ya Feng Ya ◽  
Hai Ming Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Learning Rate ◽  
Prediction Problem ◽  
Previous Time

Because the choice and important of learning rate , the higher of η and the faster convergence it will be, but it may cause instability or function vibration if is too high; if is lower, although it may avoid instability, the speed of function convergence will reduce. In order to solve the contradiction, we introduce a variable of , and if the this time is the same as that of the previous time, the weighted summation value will increase and it results in the regulation speed of right value at the stable regulation; and if the this time is contrary to that of the previous time, it indicates that a certain vibration and now the result of summation will make the value of decrease to play a role in stability and increase the speed of function convergence.

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.

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