scholarly journals Prediction Model of Carbon-containing Pellet Reduction Metallization Ratio Using Neural Network and Genetic Algorithm

2021 ◽  
Vol 61 (6) ◽  
pp. 1915-1926
Author(s):  
Wei Zhang ◽  
Feng Wang ◽  
Nan Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Model

Weight Loss Prediction Model for Pig Carcass Based on a Genetic Algorithm Back- Propagation Neural Network

2020 ◽  
Vol 63 (4) ◽  
pp. 1071-1077
Author(s):  
Chenyang Sun ◽  
Lusheng Chen ◽  
Yinian Li ◽  
Hao Yao ◽  
Nan Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Weight Loss ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Time Interval ◽  
Spraying Parameters ◽  
Pig Carcasses

HighlightsWe propose five spraying parameters according to the characteristics of pig carcasses in the spray-chilling process.A prediction model for pig carcass weight loss, based on a genetic algorithm back-propagation neural network, is proposed to reveal the relationship between weight loss and spraying parameters.To study the effects of various spraying parameters on weight loss, an automatic spray-chilling device was designed, which can modify up to five spraying parameters.Abstract. Because the weight loss of a pig carcass in the spray-chilling process is easily affected by the spraying frequency and duration, a prediction model for weight loss based on a genetic algorithm (GA) back-propagation (BP) neural network is proposed in this article. With three-way crossbred pig carcasses selected as the test materials, the duration and time interval of high-frequency spraying, the duration and time interval of low-frequency spraying, and the duration of a single spray were selected as inputs to the network model. The weight and threshold of the network were then optimized by the GA. The prediction model for pig carcass weight loss established by the GA BP neural network yielded a correlation coefficient of R = 0.99747 between the network output value of the test samples and the target value. Weight loss prediction by the model is feasible and allows better expression of the nonlinear relationship between weight loss and the main controlling factors. The results can be a reference for chilled meat production. Keywords: BP neural network, Genetic algorithm, Pig carcass, Predictive model, Weight loss

scholarly journals Optimized Erosion Prediction with MAGA Algorithm Based on BP Neural Network for Submerged Low-Pressure Water Jet

2020 ◽  
Vol 10 (8) ◽  
pp. 2926
Author(s):  
Yanzhen Chen ◽  
Yihuai Hu ◽  
Shenglong Zhang ◽  
Xiaojun Mei ◽  
Qingguo Shi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Water Jet ◽  
Erosion Prediction ◽  
Pressure Water ◽  
Erosion Test ◽  
Bp Model

In order to accurately predict the erosion effect of underwater cleaning with an angle nozzle under different working conditions, this paper uses refractory bricks to simulate marine fouling as the erosion target, and studies the optimized erosion prediction model by erosion test based on the submerged low-pressure water jet. The erosion test is conducted by orthogonal experimental design, and experimental data are used for the prediction model. By combining with statistical range and variance analysis methods, the jet pressure, impact time and jet angle are determined as three inputs of the prediction model, and erosion depth is the output index of the prediction model. A virtual data generation method is used to increase the amount of input data for the prediction model. This paper also proposes a Mind-evolved Advanced Genetic Algorithm (MAGA), which has a reliable optimization effect in the verification of four stand test functions. Then, the improved back-propagating (BP) neural network prediction models are established by respectively using Genetic Algorithm (GA) and MAGA optimization algorithms to optimize the initial thresholds and weights of the BP neural network. Compared to the prediction results of the BP and GA-BP models, the R2 of the MAGA-BP model is the highest, reaching 0.9954; the total error is reduced by 47.31% and 35.01%; the root mean square error decreases by 51.05% and 31.80%; and the maximum absolute percentage error decreases by 65.79% and 64.01%, respectively. The average prediction accuracy of the MAGA-BP model is controlled within 3%, which has been significantly improved. The results show that the prediction accuracy of the MAGA-BP prediction model is higher and more reliable, and the MAGA algorithm has a good optimization effect. This optimized erosion prediction method is feasible.

The Establishment of Prediction Model for Soil Liquefaction Based on the Seismic Energy Using the Neural Network

2021 ◽  
Author(s):  
yan zhang ◽  
wenhui chu ◽  
Mahmood Ahmad
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Seismic Energy ◽  
Soil Liquefaction ◽  
Nonlinear Relationship ◽  
Weakly Nonlinear ◽  
Fitness Value ◽  
Validation Set

Abstract In the development of the prediction model for soil liquefaction, compared to the stress-based method, the energy-based methods proposed and developed in recent years are closer to the essence of soil liquefaction which is about the energy dissipation. Therefore, considering the weak nonlinear relationship found by the previous research, the fuzzy neural network (FNN) and BP neural network (BPNN) were adopted to try to obtain a prediction model which is the most proper to this nonlinear relationship. Firstly, the database including 284 cases obtained from laboratory test was divided into three separate groups denoted as training, validation set and testing sets by the ratio of 5:1:1; then, the FNN model and BPNN model were iterated to determine the model parameter by referring to the variation of fitness value and relative error of validation set; at the same time, the optimization algorithm of genetic algorithm (GA) was adopted to BPNN to find the best coefficients; besides, the parameter of \({C}_{c}\) and \({D}_{50}\) was respectively excluded from the database to test their influence degree according to the prediction error; finally, 6 prediction results of FNN and genetic algorithm BP neural network (GABP) were compared with the previously proposed models. The results showed that the relationship of capacity energy to the influencing parameters could not be fitted as a fully linear relationship; the FNN model can learn the role of \({C}_{c}\) in affecting the capacity energy while the GABP model needs not to take it into account; the FNN and GABP model all fitted a good weakly nonlinear relationship for the capacity energy, and the GABP model is a better prediction model for capacity energy so far.

Prediction and optimization model of activated carbon double layer capacitors based on improved heuristic approach genetic algorithm neural network

2018 ◽  
Vol 35 (4) ◽  
pp. 1625-1638 ◽  
Author(s):  
Zhen Yang ◽  
Yun Lin ◽  
Xingsheng Gu ◽  
Xiaoyi Liang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Activated Carbon ◽  
Prediction Model ◽  
Electrochemical Properties ◽  
Double Layer ◽  
Process Parameters ◽  
Electrode Material ◽  
Content Type ◽  
Double Layer Capacitors

PurposeThe purpose of this paper is to study the electrochemical properties of electrode material on activated carbon double layer capacitors. It also tries to develop a prediction model to evaluate pore size value.Design/methodology/approachBack-propagation neural network (BPNN) prediction model is used to evaluate pore size value. Also, an improved heuristic approach genetic algorithm (HAGA) is used to search for the optimal relationship between process parameters and electrochemical properties.FindingsA three-layer ANN is found to be optimum with the architecture of three and six neurons in the first and second hidden layer and one neuron in output layer. The simulation results show that the optimized design model based on HAGA can get the suitable process parameters.Originality/valueHAGA BPNN is proved to be a practical and efficient way for acquiring information and providing optimal parameters about the activated carbon double layer capacitor electrode material.

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.

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