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.

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 Back-Propagation Neural Network and Genetic Algorithm to the Preparation Processing of the Mg,Al-Hydrotalcite/Polymer Nanocomposite

2008 ◽  
Vol 368-372 ◽  
pp. 1680-1682
Author(s):  
Qiang Luo ◽  
Qing Li Ren
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Local Minimum ◽  
Layer Structure ◽  
Back Propagation ◽  
Polymer Nanocomposite ◽  
Back Propagation Neural Network ◽  
Percentage Elongation ◽  
Technological Factors ◽  
Propagation Network

The three-layer structure back-propagation network model based on the non-linear relationship between the break percentage elongation of the Mg,Al-hydrotalcite/PE nanocomposites and the technological factors was established. And in order to accelerate the converging rate and avoid the local minimum, dimensionality reduction and pre-whitening methods were used. Moreover, the optimum technological process parameters were optimized with genetic algorithm. And the results show that using both the back propagation neural networks and genetic algorithm is very efficient for the prediction of the break percentage elongation of the Mg,Al-hydrotalcite/PE nanocomposite.

Application of Improved Back-Propagation Neural Network to the Technologic Processing of Korshunskite Whiskers

2014 ◽  
Vol 602-603 ◽  
pp. 312-315
Author(s):  
Qing Li Ren ◽  
Qiang Luo ◽  
Miao Miao Yang
Keyword(s):  
Atmospheric Pressure ◽  
Raw Materials ◽  
Layer Structure ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Reaction Method ◽  
Technological Factors ◽  
One Step ◽  
The One ◽  
Propagation Network

The korshunskite samples were prepared in precipitation by the one-step reaction method at atmospheric pressure. The three-layer structure back-propagation network model based on the non-linear relationship between the amount of the korshunskite whiskers and the technological factors, such as the adding amount of raw materials NaOH, MgCl2, MgO, and reaction temperature, is established. And the results show that the improved back propagation neural networks model is very efficient for predication of the korshunskite whiskers preparation.

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.

Prediction of Shear Strength for Subgrade Soil under Freezing-Thawing Based on Back Propagation Neural Network

2014 ◽  
Vol 926-930 ◽  
pp. 610-614 ◽  
Author(s):  
Jing Long Chen ◽  
Pei Feng Cheng ◽  
Chuan Jun Yin
Keyword(s):  
Neural Network ◽  
Shear Strength ◽  
Transfer Function ◽  
Sandy Soil ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Friction Angle ◽  
Back Propagation Neural Network ◽  
Linear Transfer Function ◽  
Propagation Network

Soil samples are taken from two experimental roads in Heilongjiang province for the test. Then a prediction of shear strength is carried out, basing on a three-layer BP (back propagation) network in Matlab, the hidden layer, output layer and training function of which adopt non-linear transfer function tansig, linear transfer function purelin, and trainbfg function respectively. It is found workable to predict factors influencing shearing strength using BP neural network with given soil properties. Prediction results of cohesion strength for clay show a better performance than those for sandy soil, while results of friction angle for sandy soil are better than those for clay. It is indicated that BP neural network does a better work in predicting the friction angle than that of cohesion.

Reliability Inference for Products of Weibull Distribution by Using Artificial Neural Network Formal Description

2006 ◽  
Vol 326-328 ◽  
pp. 573-576
Author(s):  
Yung Chung Chen ◽  
Pei Hsi Lee ◽  
Chien Ming Chen
Keyword(s):  
Weibull Distribution ◽  
Research Result ◽  
Back Propagation ◽  
Accelerated Life Testing ◽  
Learning Ability ◽  
Accelerated Life ◽  
Input Variables ◽  
Hidden Layer ◽  
Propagation Network ◽  
Self Learning

Back-propagation network (BPN) has the advantage of simulating a nonlinear system that is difficult to describe by a physical model. This study introduces a back-propagation network methodology to estimate the accelerated life reliability. The environmental stresses and failure times are chosen as the input variables. An optimum prediction system is acquired by adjusting the number of neurons in the hidden layer and the output layer of neural networks. For a numerical example, the developed BPN architecture is applied to real accelerated life testing data of the STNLCD modules which are distributed as a Weibull distribution. By the research result, we can have the conclusion that the BPN methodology is practical to make the reliability inference with the advantages of self-learning ability even without mathematics models.

Quality Prediction Model Based on PCA-BP Neural Network for Tobacco Leaves Redrying Process

2011 ◽  
Vol 201-203 ◽  
pp. 1627-1631
Author(s):  
Jian Kang Yin ◽  
Chang Hua Chen ◽  
Jing Min Li ◽  
Fei Zhang ◽  
Jin Yao
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Principal Component ◽  
Absolute Error ◽  
Quality Prediction ◽  
Tobacco Leaves ◽  
Model Based ◽  
Input Variables

Aiming to the problem that is very difficult to establish the mechanism model of quality for the process of tobacco leaves redrying, this paper proposes a quality prediction model based on principal component analysis (PCA) and improved back propagation (BP)neural network for tobacco leaves redrying process. Firstly, 12 input variables are confirmed by analyzing the factors on quality of tobacco leaves redrying process. Second, the methods of PCA is used to eliminate the correlation of original input layer data, in which 12 input variables are transformed into 6 uncorrelated indicators. Then, the quality prediction model based on improved BP neural network is established. Finally, a simulation experiment is conducted and the average prediction error is as low as 1.03%, the absolute error for forecasting is fluctuated in the range of 0.16% - 2.49%. The result indicates that the model is simpler and has higher stability for prediction, which can completely meet the actual requirements of the tobacco leaves redrying process.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Back Propagation ◽  
Second Step ◽  
Heating Value ◽  
Network Training ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

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