Application of BP Neural Network Model Based on Improved DNA Genetic Algorithm to Tool Wear Monitoring

2012 ◽  
Vol 6 (1) ◽  
pp. 756-759
Author(s):  
Nie Shuzhi ◽  
Zhong Yanhua
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Tool Wear ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Tool Wear Monitoring ◽  
Model Based ◽  
Wear Monitoring ◽  
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.

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