scholarly journals Numerical Study on RC Multilayer Perforation with Application to GA-BP Neural Network Investigation

2020 ◽  
Vol 6 (4) ◽  
pp. 806-819
Author(s):  
Weiwei Sun ◽  
Ze Shi ◽  
BingCheng Chen ◽  
Jun Feng
Keyword(s):  
Neural Network ◽  
Layer Thickness ◽  
Test Data ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Deflection Angle ◽  
Numerical Study ◽  
Residual Velocity ◽  
Parametric Analyses ◽  
Concrete Target

The finite element model of projectile penetrating multi-layered reinforced concrete target was established via LS-DYNA solver. The penetration model was validated with the test data in terms of residual velocity and deflection angle.  Parametric analyses were carried out through the verified penetration model. Seven influential factors for penetration conditions, including the initial velocity of projectile, initial angle of attack of projectile, initial dip angle of projectile, the first layer thickness of concrete target, the residual layer thickness of concrete target, target distance and the layer number of concrete target, were put emphasis on further analysis. Furthermore, the influence of foregoing factors on residual velocity and deflection angle of projectile were numerically obtained and discussed. Based on genetic algorithm, the BP neural network model was trained by 263 sets of data obtained from the parametric analyses, whereby the prediction models of residual velocity and attitude angle of projectile under different penetration conditions were achieved. The error between the prediction data obtained by this model and the reserved 13 sets of test data is found to be negligible.

scholarly journals Prediction of the First Weighting from the Working Face Roof in a Coal Mine Based on a GA-BP Neural Network

2019 ◽  
Vol 9 (19) ◽  
pp. 4159
Author(s):  
Tan ◽  
Yang ◽  
Chang ◽  
Zhao
Keyword(s):  
Neural Network ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Initial Pressure ◽  
Mining Area ◽  
Mine Pressure ◽  
Mine Production ◽  
Production Safety ◽  
Matlab Programming

The accidents caused by roof pressure seriously restrict the improvement of mines and threaten production safety. At present, most coal mine pressure forecasting methods still rely on expert experience and engineering analogies. Artificial neural network prediction technology has been widely used in coal mines. This new approach can predict the surface pressure on the roof, which is of great significance in coal mine production safety. In this paper, the mining pressure mechanism of coal seam roofs is summarized and studied, and 60 sets of initial pressure data from multiple working surfaces in the Datong mining area are collected for gray correlation analysis. Finally, 12 parameters are selected as the input parameters of the model. Suitable back propagation (BP) and GA(genetic algorithm)-BP initial roof pressure prediction models are established for the Datong mining area and trained with MATLAB programming. By comparing the training results, we found that the optimized GA-BP model has a larger determination coefficient, smaller error, and greater stability. The research shows that the prediction method based on the GA-BP neural network model is relatively reliable and has broad engineering application prospects as an auxiliary decision-making tool for coal mine production safety.

A Model Based on BP Neural Network for Audible Noise Prediction

2014 ◽  
Vol 986-987 ◽  
pp. 1356-1359
Author(s):  
You Xian Peng ◽  
Bo Tang ◽  
Hong Ying Cao ◽  
Bin Chen ◽  
Yu Li
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Transmission Lines ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Absolute Error ◽  
Research Area ◽  
Noise Prediction ◽  
Bp Network ◽  
Model Based

Audible noise prediction is a hot research area in power transmission engineering in recent years, especially come down to AC transmission lines. The conventional prediction models at present have got some problems such as big errors. In this paper, a prediction model is established based on BP network, in which the input variables are the four factors in the international common expression of power line audible noise and the noise value is the output. Take multiple measured power lines as an example, a train is made by the BP network and then the prediction model is set up in the hidden layer of the network. Using the trained model, the audible noise values are predicted. The final results show that the average absolute error in absolute terms of the values by the audible noise prediction model based on BP neural network is 1.6414 less than that predicted by the GE formula.

Research on Traffic Delay Predicting at Signalized Intersection Based on Neural Network

2013 ◽  
Vol 427-429 ◽  
pp. 901-904 ◽  
Author(s):  
Chang Jiang Zheng ◽  
De Gang Lin ◽  
Shu Kang Zheng ◽  
Shu Yan Chen
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Signalized Intersection ◽  
Actual Data ◽  
Traffic Delays ◽  
Traffic Delay ◽  
Strong Ability

Available traffic delays prediction models for signalized intersection tend to predict the traffic delays under certain conditions and they are weak in adapt to different situation. In the paper, based on the theories of BP neural network, a network model, having a strong ability to adapt to different conditions, for traffic delay in average hours at a signalized intersection is established. It is trained and tested utilizing the data of traffic delay in average hours at a certain entrance of a signalized intersection. The predicted results and the actual data are compared with each other and the results prove the reliability and effectiveness of BP neural network in predicting traffic delays.

Short-Term Wind Power Dynamic Prediction Based on GA-BP Neural Network

2014 ◽  
Vol 986-987 ◽  
pp. 524-528 ◽  
Author(s):  
Ting Jing Ke ◽  
Min You Chen ◽  
Huan Luo
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Model Parameters ◽  
Short Term ◽  
Dynamic Prediction ◽  
Power Dynamic ◽  
Prediction Approach

This paper proposes a short-term wind power dynamic prediction model based on GA-BP neural network. Different from conventional prediction models, the proposed approach incorporates a prediction error adjusting strategy into neural network based prediction model to realize the function of model parameters self-adjusting, thus increase the prediction accuracy. Genetic algorithm is used to optimize the parameters of BP neural network. The wind power prediction results from different models with and without error adjusting strategy are compared. The comparative results show that the proposed dynamic prediction approach can provide more accurate wind power forecasting.

Prediction of Yarn Unevenness Based on BP Neural Network

2012 ◽  
Vol 627 ◽  
pp. 329-332
Author(s):  
Hui Jun Li ◽  
Xin Hou Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Test Results ◽  
Bp Neural Networks

The objective of this research is to predict yarn unevenness. The model of predicting yarn unevenness is built based on BP neural network. The BP neural networks are trained with HVI test results of cotton and USTER TENSOJET 5-S400 test results of yarn. The results show prediction models based on BP neural network are very precise and efficient.

scholarly journals Sales Forecasting Model Based on BP Neural Network Optimized by Improved Immune Genetic Algorithm

2021 ◽  
Author(s):  
Kunyu Cao ◽  
Yongdang Chen ◽  
Xinxin Song ◽  
Shan Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Forecasting Model ◽  
Antibody Concentration ◽  
Immune Genetic Algorithm ◽  
Model Based ◽  
Neural Network Prediction ◽  
Network Prediction

Abstract A new sales forecasting model based on an Improved Immune Genetic Algorithm (IIGA), IIGA that optimizes the BPNN (IIGA-BP) has been proposed. The IIGA presents a new way of population initialization, a regulatory mechanism of antibody concentration, and a design method of adaptive crossover operator and mutation operator, which effectively improved the convergence ability and optimization anility of IIGA. And IIGA can optimize the BPNN’s initial weights and threshold and improve the randomness of network parameters as well as the drawbacks that lead to output instability of BPNN and easiness into local minimum value. It taking the past records of sales figures of a certain steel enterprise as an example, utilizing the Matlab to construct the BP neural network, Immune Genetic Algorithm that optimizes the BPNN (IGA-BP), IGA-BP neural network, and IIGA-BP neural network prediction models for simulation and comparative analysis. The experiment demonstrates that IIGA-BP neural network prediction model possessing a higher prediction accuracy and more stable prediction effects.

Predicition of GRT Fiber-Rubberized Haydite Concrete Compressive Strength Based on Multiple Regreeion Analysis and BP Neural Network

2012 ◽  
Vol 472-475 ◽  
pp. 60-65
Author(s):  
Bing Hua Xia ◽  
Yuan Cai Liu ◽  
De Bin Zhu
Keyword(s):  
Neural Network ◽  
Compressive Strength ◽  
Multiple Regression ◽  
Test Data ◽  
Bp Neural Network ◽  
Intensity Level ◽  
Regression Equations ◽  
Strength Based ◽  
Neural Network Theory ◽  
Bp Neural Network Model

Experiment with intensity level for the LC30 ceramsite concrete as the research object, changing the content of cement, GRT fiber, rubber powder by the orthogonal test to configure GRT fiber—rubberized haydite concrete samples, maintenance samples 7d and 28d in standard conditions and respectively testing their standard compressive strength. Through the analysis of the test data, using multiple regression analysis established the GRT fiber—rubberized haydite concrete 7d and 28d standard compressive strength regression formulas.By means of BP neural network theory combine MATLAB programme established GRT fiber—rubberized haydite concrete 7d and 28d standard compressive strength neural network model.Finally using 3 groups new test data to compare the value of multiple regression equations and BP neural network’s predicted value.The results indicate that the multiple regression equations and BP neural network model are availabled.

Prediction Models of Short-Term Traffic Flow Based on Neural Network

2013 ◽  
Vol 671-674 ◽  
pp. 2908-2911 ◽  
Author(s):  
Chao Jun Dong ◽  
Ang Cui
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Topology ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Measured Data ◽  
Short Term ◽  
Hidden Layer ◽  
Bp Neural Network Model

For the city’s road conditions, a nonlinear regression prediction model based on BP Neural Network was built. The simulation shows it has good adaptability and strong nonlinear mapping ability. Using the wavelet basis function as hidden layer nodes transfer function, a BP-Neural- Network-topology-based Wavelet Neural Network model was proposed. The model can overcome the defects of the BP Neural Network model that easy to fall into local minimum and cannot perform global search. The feasibility of the model was proved using measured data from yingbin avenue in jiangmen city.

scholarly journals A Stock Prices Prediction Approach Via Neural Network by Several Investor Indicators

2019 ◽  
pp. 477-484
Author(s):  
Yahui Chen ◽  
Zhan Wen ◽  
Qi Li ◽  
Yuwen Pan ◽  
Xia Zu ◽  
...  
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Bp Neural Network ◽  
Stock Prices ◽  
Stock Price ◽  
Quantitative Data ◽  
Prediction Models ◽  
Investor Behavior ◽  
Prediction Approach ◽  
The Impact

The prediction of stock indicators such as prices, trends and market indices is the focus of researchers. However, stock market has the characteristics of high noise and non-linearity. Generally, linear algorithms are not good for predicting stock market indicators. Therefore, BP neural network, a model suitable for nonlinear task, is widely used in stock market forecasting. However, many BP neural network prediction models are only based on historical stock quantitative data, and do not consider the impact of investor behavior on the stock market. Therefore, based on historical stock data and quantitative data of investor behavior of ten selected Chinese stocks, this paper trains a three-layer BP neural network to predict the stock prices such as the highest price ,the opening price ,the closing price, the lowest price in a short term. And then, the model that incorporates the investor behavior indicator is compared with the model that is not added. The results show that investor behavior indicators can improve the accuracy and generalization of the stock price forecasting model effectively, especially when the model based on stock quantitative data has a poor prediction accuracy on the test set.

scholarly journals Research on Credit Card Default Prediction Based on k-Means SMOTE and BP Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ying Chen ◽  
Ruirui Zhang
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Bp Neural Network ◽  
Credit Card ◽  
Prediction Models ◽  
Financial Institution ◽  
Classification Performance ◽  
Default Prediction ◽  
Credit Card Default ◽  
Default Data

Aiming at the problem that the credit card default data of a financial institution is unbalanced, which leads to unsatisfactory prediction results, this paper proposes a prediction model based on k-means SMOTE and BP neural network. In this model, k-means SMOTE algorithm is used to change the data distribution, and then the importance of data features is calculated by using random forest, and then it is substituted into the initial weights of BP neural network for prediction. The model effectively solves the problem of sample data imbalance. At the same time, this paper constructs five common machine learning models, KNN, logistics, SVM, random forest, and tree, and compares the classification performance of these six prediction models. The experimental results show that the proposed algorithm can greatly improve the prediction performance of the model, making its AUC value from 0.765 to 0.929. Moreover, when the importance of features is taken as the initial weight of BP neural network, the accuracy of model prediction is also slightly improved. In addition, compared with the other five prediction models, the comprehensive prediction effect of BP neural network is better.

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