Application of Improved BP Neural Network in the Preparation Processing of the SrTiO3 Nanocrystalline

2007 ◽  
Vol 336-338 ◽  
pp. 2497-2500
Author(s):  
Qiang Luo ◽  
Qing Li Ren
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Raw Material ◽  
Learning Speed ◽  
Main Component Analysis ◽  
Input Variables ◽  
Main Component

A three-layer back-propagation neural network model based on the non-linear relationship between the size of the SrTiO3 nanocrystalline and the technology factors, such as reaction time, reaction temperature, raw material adding amount of NaOH and SrCl2, and the rate of TiCl4/Hl, was established. Moreover, in order to accelerate the converging rate and avoid the non-converging situation, the momentum terms are introduced. Besides, the variable learning speed is adopted. At the same time, the input variables were pretreated by using the main component analysis firstly. And the results show that the improved back-propagation neural network model is very efficient for predication of the SrTiO3 nanocrystalline size.

Application of Improved BP Neural Network in the Preparation Processing of the CaCO3 Nanocrystalline

2017 ◽  
Vol 726 ◽  
pp. 338-342 ◽  
Author(s):  
Qiang Luo ◽  
Qing Li Ren
Keyword(s):  
Bp Neural Network ◽  
Layer Structure ◽  
Back Propagation ◽  
Raw Material ◽  
Technological Factors ◽  
Learning Speed ◽  
Main Component Analysis ◽  
Input Variables ◽  
Main Component ◽  
Propagation Network

A three-layer structure back-propagation network model based on the non-linear relationship between the size of the CaCO3 nanocrystalline and the technological factors, such as reaction time, reaction temperature, raw material adding amount of NaCO3 and CaCl2, was established. Moreover, in order to accelerate the converging rate and avoid the non-converging situation, the momentum terms are introduced. Besides, the variable learning speed is adopted. At the same time, the input variables were pretreated by using the main component analysis firstly. And the results show that the improved back propagation neural networks model is very efficient for predication of the CaCO3 nanocrystalline size.

scholarly journals Back-propagation neural network model to predict visibility at a road link-level

2020 ◽  
Vol 8 ◽  
pp. 100250
Author(s):  
Venkata R. Duddu ◽  
Srinivas S. Pulugurtha ◽  
Ajinkya S. Mane ◽  
Christopher Godfrey
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Back Propagation Neural Network

Application of the Back Propagation Neural Network Model on the Supplier Evaluation in E-Manufacturing Environment

2009 ◽  
Vol 16-19 ◽  
pp. 174-177
Author(s):  
Jian Chen ◽  
Ming Hong Wu ◽  
Ilias Oraifige
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Evaluation System ◽  
Supplier Selection ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Supplier Evaluation ◽  
Test Results ◽  
Model Training

The supplier evaluation is a key section of the intelligent internet supplier selection & evaluation system. The model used for supplier evaluation is Back Propagation Neural Network model which is introduced in the paper. The paper started with the brief introduction of the intelligent internet supplier selection & evaluation system. It provides a outline of the research project and then it concentrated to introduce the application of the BP NN model for supplier evaluation. The application introduced in the paper will include the design of the BP NN model, Training of the BP NN model and test results.

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