Based on Genetic Algorithms and Artificial Neural Network Algorithms to Optimize the Structure Design and Implementation of Crane

2012 ◽  
Vol 490-495 ◽  
pp. 2120-2124
Author(s):  
Xiao Mo Yu ◽  
Xiao Ping Liao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Dynamic Behavior ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Structure System ◽  
Sample Data

This paper by using the finite element method, orthogonal test method, BP neural network and genetic algorithm to optimization of crane structure system. A dynamic optimal computational model for the complex structure system with genetic algorithm (GA)and BP neural network(NN)was presented．Instead of the traditional finite element model，this model can be used for the fast re-analysis for the vibration system．Firstly，the harmonic response kinetics analysis can be processed on a crane structure system and can find out them ode frequency which has the strongest effect on the system dynamic behavior．Secondly，from the sensitivity analysis，the design variables which are more sensitive to the system dynamic behavior can be confirmed as the input variables． Then an orthogonal experimentation was used in choosing the training sample data and the sample data was calculated through the finite element model．The artificial neural network model which presented the dynamic behavior of the structure vibration was established．At last，the neural network model will be optimized through the generic algorithm and the optimal parameters of the structure dynamic behavior will be obtained．

scholarly journals Prediction of Developing Modern Agriculture Demands for the Agricultural Scientific Research Institutions Services Based on BP Artificial Neural Network

2019 ◽  
Vol 131 ◽  
pp. 01122
Author(s):  
Hongyan Sun ◽  
Hongwen Bi ◽  
Jingyuan Wang ◽  
Yu Zhang ◽  
Yanqi Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Scientific Research ◽  
Research Institutions ◽  
Bp Artificial Neural Network ◽  
Modern Agriculture ◽  
Bp Neural Network Model

BP artificial neural network model is used to predict developing modern agriculture demands for the agricultural scientific research institutions services. Starting from the brief introduction of the usages of BP neural network, we analyzed the demand factors of the agricultural scientific research institutions services and the affective elements of the demands, use the BP neural network model to predict, and then run the BP neural network model on the MATLAB platform, and finally carry out the case studies of Heilongjiang Province.

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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