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.

Prediction of Sand Thickness Using Seismic Attributes Based on BP-Neural Network

2012 ◽  
Vol 524-527 ◽  
pp. 180-183
Author(s):  
Feng Gao
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Seismic Attributes ◽  
Bp Network ◽  
Sand Body ◽  
Precise Prediction ◽  
Energy Maximum ◽  
Bp Model ◽  
Rms Amplitude

Total energy, maximum peak amplitude and RMS amplitude are sensitive to sand body, and they are non-linear relations with sand thickness. In this study, a three-layer BP neural network is employed to build the prediction model. Nine samples were analyzed by three-layer BP network. The relationships were produced by BP network between sand thickness and the three seismic attributes. The precise prediction results indicate that the three-layer BP network based modeling is a practically very useful tool in prediction sand thickness. The BP model provided better accuracy in prediction than other methods.

scholarly journals Prediction of the Deformation of Aluminum Alloy Drill Pipes in Thermal Assembly Based on a BP Neural Network

2022 ◽  
Vol 12 (2) ◽  
pp. 757
Author(s):  
Xiaofeng Wang ◽  
Baochang Liu ◽  
Jiaqi Yun ◽  
Xueqi Wang ◽  
Haoliang Bai
Keyword(s):  
Neural Network ◽  
Aluminum Alloy ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Thermal Deformation ◽  
Heating Temperature ◽  
Cooling Water ◽  
Interference Fit ◽  
Steel Joint

The connection between the steel joint and aluminum alloy pipe is the weak part of the aluminum alloy drill pipe. Practically, the interference connection between the aluminum alloy rod and the steel joint is usually realized by thermal assembly. In this paper, the relationship between the cooling water flow rate, initial heating temperature and the thermal deformation of the steel joint in interference thermal assembly was studied and predicted. Firstly, the temperature data of each measuring point of the steel joint were obtained by a thermal assembly experiment. Based on the theory of thermoelasticity, the analytical solution of the thermal deformation of the steel joint was studied. The temperature function was fitted by the least square method, and the calculated value of radial thermal deformation of the section was finally obtained. Based on the BP neural network algorithm, the thermal deformation of steel joint section was predicted. Besides, a prediction model was established, which was about the relationship between cooling water flow rate, initial heating temperature and interference. The magnitude of interference fit of steel joint was predicted. The magnitude of the interference fit of the steel joint was predicted. A polynomial model, exponential model and Gaussian model were adopted to predict the sectional deformation so as to compare and analyze the predictive performance of a BP neural network, among which the polynomial model was used to predict the magnitude of the interference fit. Through a comparative analysis of the fitting residual (RE) and sum of squares of the error (SSE), it can be known that a BP neural network has good prediction accuracy. The predicted results showed that the error of the prediction model increases with the increase of the heating temperature in the prediction model of the steel node interference and related factors. When the cooling water velocity hit 0.038 m/s, the prediction accuracy was the highest. The prediction error increases with the increase or decrease of the velocity. Especially when the velocity increases, the trend of error increasing became more obvious. The analysis shows that this method has better prediction accuracy.

scholarly journals Study on MPGA-BP of Gravity Dam Deformation Prediction

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaoyu Wang ◽  
Kan Yang ◽  
Changsong Shen
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Population Genetic ◽  
Prediction Accuracy ◽  
Gravity Dam ◽  
Hydraulic Structures ◽  
Deformation Prediction ◽  
Multiple Population ◽  
Population Genetic Algorithm

Displacement is an important physical quantity of hydraulic structures deformation monitoring, and its prediction accuracy is the premise of ensuring the safe operation. Most existing metaheuristic methods have three problems: (1) falling into local minimum easily, (2) slowing convergence, and (3) the initial value’s sensitivity. Resolving these three problems and improving the prediction accuracy necessitate the application of genetic algorithm-based backpropagation (GA-BP) neural network and multiple population genetic algorithm (MPGA). A hybrid multiple population genetic algorithm backpropagation (MPGA-BP) neural network algorithm is put forward to optimize deformation prediction from periodic monitoring surveys of hydraulic structures. This hybrid model is employed for analyzing the displacement of a gravity dam in China. The results show the proposed model is superior to an ordinary BP neural network and statistical regression model in the aspect of global search, convergence speed, and prediction accuracy.

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

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.

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.

Study on Prediction Model to Terminal Demand Based on Improved Genetic Neural Network

2013 ◽  
Vol 798-799 ◽  
pp. 506-509
Author(s):  
Chen Huang ◽  
Yan Huo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Marketing Strategy ◽  
Bp Neural Network ◽  
Local Minimum ◽  
Important Prerequisite ◽  
Potential Demand ◽  
Genetic Neural Network

Reasonable forecast of terminal demand is one important prerequisite of the deepening development of 3G-device industry. Through the analysis of characteristics about terminal consumption, one prediction model was used for 3G terminal demand with an artificial neural network mode in which genetic algorithm solved the problem about local minimum value of BP neural network and the input set was solved by fuzzy method, and before that the feature vectors were in the form of inconsistency measure. The corresponding marketing strategy can be put forward pertinently to facilitate fully the potential demand group and improve operational efficiency.

scholarly journals Research on prediction model of thermal and moisture comfort of underwear based on principal component analysis and Genetic Algorithm–Back Propagation neural network

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Pengpeng Cheng ◽  
Daoling Chen ◽  
Jianping Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Principal Component Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Back Propagation Neural Network

AbstractIn order to improve the efficiency and accuracy of thermal and moisture comfort prediction of underwear, a new prediction model is designed by using principal component analysis method to reduce the dimension of related variables and eliminate the multi-collinearity relationship between variables, and then inputting the converted variables into genetic algorithm (GA) and BP neural network. In order to avoid the problems of slow convergence speed and easy falling into local minimum of Back Propagation (BP) neural network, this paper adopted GA to optimize the weights and thresholds of BP neural network, and utilized MATLAB software to program, and established the prediction models of BP neural network and GA–BP neural network. To verify the superiority of the model, the predicted result of GA–BP, PCA–BP and BP are compared with GA–BP neural network. The results show that PCA could improve the accuracy and adaptability of GA–BP neural network for thermal and moisture comfort prediction. PCA–GA–BP model is obviously superior to GA–BP, PCA–BP, BP, SVM and K-means prediction models, which could accurately predict thermal and moisture comfort of underwear. The model has better accuracy prediction and simpler structure.

Sign in / Sign up

Export Citation Format

Share Document