Adaptive dynamic wavelength and bandwidth allocation algorithm based on error-back-propagation neural network prediction

2019 ◽  
Vol 437 ◽  
pp. 276-284 ◽  
Author(s):  
Bo Liu ◽  
Lijia Zhang ◽  
Fu Wang ◽  
Ming Liu ◽  
Yaya Mao ◽  
...  
Keyword(s):  
Neural Network ◽  
Bandwidth Allocation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Allocation Algorithm ◽  
Adaptive Dynamic ◽  
Error Back Propagation ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Bandwidth Allocation Algorithm

scholarly journals Method of Predicting Ore Dilution Based on a Neural Network and Its Application

2020 ◽  
Vol 12 (4) ◽  
pp. 1550 ◽  
Author(s):  
Xingdong Zhao ◽  
Jia’an Niu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Relative Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Average Deviation ◽  
Graph Method ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
The Difference

A back-propagation neural network prediction model with three layers and six neurons in the hidden layer is established to overcome the limitation of the equivalent linear overbreak slough (ELOS) empirical graph method in estimating unplanned ore dilution. The modified stability number, hydraulic radius, average deviation of the borehole, and powder factor are taken as input variables and the ELOS of quantified unplanned ore dilution as the output variable. The training and testing of the model are performed using 120 sets of data. The average fitting degree r2 of the prediction model is 0.9761, the average mean square error is 0.0001, and the relative error of the prediction is approximately 6.2%. A method of calculating the unplanned ore dilution is proposed and applied to a test stope of the Sandaoqiao lead–zinc mine. The calculated unplanned ore dilution is 0.717 m, and the relative error (i.e., the difference between calculation and measurement of 0.70 m) is 2.4%, which is better than the relative errors for the empirical graph method and numerical simulation (giving dilution values of 0.8 and 0.55 m, respectively). The back-propagation neural network prediction model is confirmed to predict the unplanned ore dilution in real applications.

Application of Radial Basis Function Neural Network on the Prediction of Urban Built-Up Area

2014 ◽  
Vol 556-562 ◽  
pp. 5308-5311
Author(s):  
Li Hua Chen ◽  
Yu Chen Wang
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Quantitative Prediction ◽  
Basic Issue ◽  
Radial Basis ◽  
Neural Network Prediction ◽  
Network Prediction

The study on the prediction of urban built-up area is the basic issue in urban planning. This paper takes the prediction of urban built-up area of Hefei city as an example, building a factor system that affects built-up area from the economic, social and environmental dimensions. Then, the paper establishes a quantitative prediction model based on the Radial Basis Function neural network. As a comparison, the paper also uses the Back Propagation neural network to predict. The results show that the Radial Basis Function neural network prediction has a higher accuracy and the prediction result is more reasonable and reliable.

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