A Nonlinear Ensemble Prediction Model Based on Genetic Algorithm for Calculation Wind of the Wind Assessment

2010 ◽  
Author(s):  
Kai-ping Lin ◽  
Xiao-yan Huang ◽  
Wei-liang Liang ◽  
Bing-lian Chen
Keyword(s):  
Genetic Algorithm ◽  
Prediction Model ◽  
Ensemble Prediction ◽  
Model Based

The Coal Slurry Pipeline Pressure Prediction Research Based on Quantum Genetic BP Neural Network

2013 ◽  
Vol 336-338 ◽  
pp. 722-727
Author(s):  
Xue Cun Yang ◽  
Yuan Bin Hou ◽  
Ling Hong Kong
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Thermal Power ◽  
Coal Slurry ◽  
Cylinder Pressure ◽  
Master Cylinder ◽  
Quantum Genetic Algorithm ◽  
Model Based

According to the coal slime pipeline blockage problem of coal gangue thermal power plant, after the analysis of the actual scene, it is sure that thick slurry pump master cylinder pressure prediction is the necessary premise of blockage prediction. The thick slurry pump master cylinder pressure prediction model is proposed, which is based on QGA-BP (Quantum genetic Algorithm BP neural network). The simulation results show that the prediction model based on QGA-BP can be used to predict the paste pump outlet pressure, and the relative error is less than 8%, which can satisfy the engineering requirement .And compared with prediction model based on GA-BP(the genetic Algorithm BP neural network), The QGA-BP prediction model is better than GA-BP model in prediction accuracy and optimization time.

scholarly journals Prediction of springback in local bending of hull plates using an optimized backpropagation neural network

2021 ◽  
Vol 12 (2) ◽  
pp. 777-789
Author(s):  
Binjiang Xu ◽  
Lei Li ◽  
Zhao Wang ◽  
Honggen Zhou ◽  
Di Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Model ◽  
Optimal Solution ◽  
Backpropagation Neural Network ◽  
Processing Efficiency ◽  
Model Based ◽  
Assembly Accuracy ◽  
Bending Process ◽  
Springback Prediction

Abstract. Springback is an inevitable problem in the local bending process of hull plates, which leads to low processing efficiency and affects the assembly accuracy. Therefore, the prediction of the springback effect, as a result of the local bending of hull plates, bears great significance. This paper proposes a springback prediction model based on a backpropagation neural network (BPNN), considering geometric and process parameters. Genetic algorithm (GA) and improved particle swarm optimization (PSO) algorithms are used to improve the global search capability of BPNN, which tends to fall into local optimal solutions, in order to find the global optimal solution. The result shows that the proposed springback prediction model, based on the BPNN optimized by genetic algorithm, is faster and offers smaller prediction error on the springback due to local bending.

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