The BP neural network model and application based on genetic algorithm

2011 ◽  
Author(s):  
Hui Li ◽  
Cai-xia Hu ◽  
Ying Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Bp Neural Network Model

Prediction of PM2.5 Mass Concentration Based on the Back Propagation (BP) Neural Network Optimized by t-Distribution Controlled Genetic Algorithm

2020 ◽  
Vol 15 (4) ◽  
pp. 432-441
Author(s):  
Peng S. Chen ◽  
Yong J. Zheng ◽  
Lin Li ◽  
Tao Jing ◽  
Xiao X. Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Mass Concentration ◽  
Monitoring And Control ◽  
Hourly Data ◽  
Bp Neural Network Model ◽  
T Distribution

In the past few years, human-health has been severely impacted from PM2.5 and has thus been a very popular topic of study. Furthermore, monitoring and control of PM2.5 are becoming one of the major environmental problems. In view of this, the present work targets at the establishment of an optimized BP neural network model based on t-distributed control genetic algorithm (BPM-TCG). Subsequently, in order to verify the performance of the proposed BPM-TCG, comparison analyses were performed among the prediction results generated from BPM-TCG, BP neural network model and BP-GA according to hourly data of PM2.5 mass concentration, analysis of corresponding meteorological factors, and gas pollutant concentrations from October 2017 to August 2018 at Qiqihar University monitoring point. The experimental results showed that BPM-TCG had the highest prediction accuracy and the best generalization ability, excellent applicability and commonality. Additionally, it may provide a basis for predicting the mass concentration of PM2.5, and thereby control and prevent the air pollution.

scholarly journals Genetic Algorithm – Back Propagation (GA-BP) Neural Network for Chlorophyll-a Concentration Inversion Using Landsat 8 OLI Data

2020 ◽  
Vol 143 ◽  
pp. 02002
Author(s):  
Qi Chen ◽  
Mutao Huang ◽  
Ronghui Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Chl A ◽  
Bp Neural Network Model

Chlorophyll-a (Chl-a) accurate inversion in inland water is important for water environmental protection. In this study, we tested the Genetic Algorithm optimized Back Propagation (GA-BP) neural network model to precisely simulated the Chl-a in an inland lake using Landsat 8 OLI images. The result show that the R2 of GA-BP neural network model has increased 28.17% compared to traditional BP neural network model. Then this GA-BP model was applied to another two scenes of Landsat 8 OLI image with the R2 of 0.961, 0.954 respectively for March 26 2018, October 26 2018. And the spatial distribution have shown a reasonable result of Chl-a variation in Lake Donghu. This study can provide a new method for Chla concentration inversion in urban lakes and support water environment protection on a large scale.

Application of Temperature Forecasting in Cupola Melting Process Based on BP Neural Network

2013 ◽  
Vol 364 ◽  
pp. 594-598
Author(s):  
Xiao Yu Sun ◽  
Jian Xin Zhou ◽  
Liang Sun ◽  
Hong Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Melting Process ◽  
Molten Iron ◽  
Algorithm Optimization ◽  
Important Indicator ◽  
Bp Neural Network Model

In cupola melting process, the temperature of molten iron is an important indicator of the quality of cast iron. Its difficult to optimize the design because of the varicosity of influencing factors in cupola melting process. This article established a BP neural network model to forecast the temperature of molten iron in cupola melting process, thus use the genetic algorithm to optimize the model. Comparing the average errors of the temperature of molten iron before and after optimization, it indicated that the BP neural network model using genetic algorithm optimization forecasted the actual situation in cupola melting more accurately.

Establishing a Genetic Algorithm-Back Propagation model to predict the pressure of girdles and to determine the model function

2020 ◽  
Vol 90 (21-22) ◽  
pp. 2564-2578
Author(s):  
Zhou Jie ◽  
Ma Qiurui
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Prediction Error ◽  
Back Propagation ◽  
Optimal Functions ◽  
Clothing Pressure ◽  
Bp Neural Network Model

A Genetic Algorithm-Back Propagation (GA-BP) neural network method has been proposed to predict the clothing pressure of girdles in different postures. Firstly, a Back Propagation (BP) neural network model was used to predict the clothing pressure based on seven parameters, and three optimal functions of the model were derived. However, the prediction error 0.85411 of the network was more than the forecast requirement of 0.5 and the optimal initial weights and thresholds for the network could not be calculated. Therefore, a GA model and the BP neural network model were combined into a new GA-BP neural network model, which was used to predict the clothing pressure based on the three optimal functions. The results showed that the prediction error for this GA-BP neural network model was 0.41652, which was less than the forecast requirement of 0.5. Hence, the model was shown to predict the girdle pressure with acceptable accuracy. Finally, the internal calculation function equation for the GA-BP neural network was derived.

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