BP Neural Network Prediction Model Research Articles

Neural Network Based Technical Analysis of Football Games

Football is the most famous sports in the world, and English Premier League is the number one league in the world for three consecutive years (Fédération internationale de football association, FIFA). It is always interesting to apply technical analysis to understand what makes the best football player and football team. In this article, we try to answer the question "what kind of tactical play is the most advanced" through statistical analysis on the game data of English Premier League in the 21-22 season. To be more specific, firstly, we applied descriptive statistics to analyze the technical and tactical play of each team, and then screen out the technical and tactical indicators that significantly affect the outcome of the game through one-way analysis of variance (ANOVA) and discriminate analysis, and the preliminary target conclusions were obtained. BP neural network was then carried out to predict the rankings of the Premier League teams by using the indicators selected by ANOVA and discriminant analysis, as input value. BP neural network prediction model is then established to predict the ranking of each team in the 22-23 season. A general conclusion and make suggestions on the planning of the technical and tactical playing methods of our country's youth soccer sports.

Design and Evaluation of Wheat Moisture Content Detection Device Based on a Stripline

The detection of the moisture content of wheat is an important index used to measure the quality and preservation of wheat. In order to rapidly and non-destructively detect the moisture content of wheat, in this study, we designed a stripline detection device that measures 151 frequency points in the 50–200 MHz frequency range with a vector network analyzer. Random forest (RF), extreme learning machine (ELM), and BP neural network prediction models were established, using the frequency, temperature, volume density and dielectric constant as input and the water content as output. It was shown that, in the frequency range 50–200 MHz, the permittivity of wheat decreases as the frequency increases, and that this is negatively correlated. The dielectric constant of wheat increases as the moisture content, temperature, and bulk density increase, and these are positively correlated. The random forest (RF) prediction model, which uses the frequency, temperature, effective dielectric constant εeff. and volume density as inputs and the wheat moisture content as the output, demonstrates the best performance. The determination coefficient (R2) = 0.99977, the mean absolute error (MAE) = 0.044368, the mean square error (MAE) = 0.0053011, and the root mean square error (RMSE) = 0.072809. This study provides a new device and method for the detection of the moisture content of wheat. The device is small and is not easily disturbed by the external environment. It can be measured in a variety of conditions and is important for the development of low-cost, high-precision, and portable devices for the detection of the moisture content of wheat.

A comparison of soil water infiltration models of moistube irrigation

As a water-saving method, moistube irrigation has been widely used. To ensure the effectiveness of moistube irrigation the development of an infiltration prediction model under moistube irrigation based on the interaction of multiple factors is required. In this paper, soil water infiltration tests with different bulk densities (1.2 g/cm³, 1.3 g/cm³, and 1.4 g/cm³) and textures (loamy sand, sandy loam, and clay loam) under different pressure heads (1m, 1.5m, and 2m) were designed, and the test data were analyzed by gray correlation theory. The pressure head, bulk density, clay content, silt content, sand content, and initial water content were determined as input variables, and the model structure was composed with two parameters of Kostiakov's model as output variables. Then, the genetic algorithm was used to optimize the back propagation neural network and the particle swarm algorithm to optimize the support vector machine. The soil moisture prediction model under moistube irrigation was established, finally the model was compared and analyzed. The results showed that the consistency effect of the two models was good. However, compared with the BP neural network prediction model optimized by genetic algorithm, the particle swarm algorithm optimized the support vector machine based moistube irrigation prediction model had higher accuracy. The results of this experiment can provide theoretical support for the exploration and modelling prediction of soil water infiltration under moistube irrigation.

Exploring the Carbon Footprint of Energy Consumption in Shanxi Province Based on the Tapio Decoupling and Grey BP Neural Network Model

This paper focuses on analyzing the carbon footprint of energy consumption in Shanxi Province, China. It utilizes data from sources such as the 'China Energy Statistical Yearbook' (2001-2022), the China Carbon Accounting Database, and the 'Shanxi Statistical Yearbook' for an exploratory analysis of energy consumption and the province's economy. The study calculates the carbon footprint of energy consumption in Shanxi Province and employs the LMDI model to identify influencing factors. Additionally, it uses the Tapio decoupling model to assess the relationship between these factors and economic development. To forecast the carbon footprint from 2022 to 2026, a grey BP neural network prediction model is applied, demonstrating improved accuracy compared to BP and grey models, with MAE and RMSE values of 2.44 and 10.91.

Improvement of an Artificial Intelligence Algorithm Prediction Model Based on the Similarity Method: A Case Study of Office Building Cooling Load Prediction

Artificial intelligence algorithms (AIAs) have gained widespread adoption in air conditioning load prediction. However, their prediction accuracy is substantially influenced by the quality of training samples. To improve the prediction accuracy of air conditioning load, this study presents an AIA prediction model based on the method of similarity sample screening. Initially, the comprehensive similarity coefficient between samples was obtained by using the gray correlation method improved with information entropy. Subsequently, a subset of closely related samples was extracted from the original dataset and employed to train the artificial intelligence prediction model. Finally, the trained AIA prediction model was used to predict the air conditioning load. The results illustrate that the method of similarity sample screening effectively improved the prediction accuracy of BP neural network (BPNN) and extreme learning machine (ELM) prediction models. However, it is essential to note that this approach may not be suitable for genetic algorithm BPNN (GABPNN) and support vector regression (SVR) models.

Mental Health Status and Influencing Factors of College Students

This study aims to address the mental health challenges brought about by the diversified development and rapid changes in society, with special attention to the psychological status of the student population. By using the SCL-90 mental health testing tool, collecting students' mental health data, and applying the fuzzy comprehensive evaluation method to analyze and evaluate students' mental health and its influencing factors in depth, the study aims to provide more effective countermeasures for students' mental health education as well as targeted teaching assistance for teachers. This study combines the BP neural network prediction model, which is committed to improving the accurate prediction of students' mental health status. The results of the study will help to assess the mental health level of students, detect and intervene in psychological crises in a timely manner, provide schools with more comprehensive mental health management and services, and promote the overall healthy growth of students.

Prediction and evaluation of polymer turbulent drag reduction efficiency based on BP neural network

In the process of oil exploitation and transportation, in order to effectively predict and control energy consumption for drag reduction of oil flow, in this paper a BP neural network was proposed based method for predicting and evaluating the turbulent drag reduction efficiency of polymers, which can greatly improve the current situation of relying on empirical formulas and low generality in polymer turbulent drag reduction efficiency prediction. Based on the experimental data sets of four commercial polymer drag-reducing agents FLOXL, М-Flowtreat, Necadd-447, and FLO MXA, obtained at different polymer concentrations, viscosity, density, and Reynolds number, a BP neural network has been established and the optimal number of neurons in the hidden layer was selected using the root mean square error (RMSE) value to obtain the optimal BP neural network prediction model. The BP neural network prediction models for the four polymer drag-reducing agents all have a good fit of 0.98 or above, and the R2 of the trained BP neural network for the Necadd-447 drag-reducing agents is 0.9949, which is the best among the four polymer drag-reducing agents. The BP neural network established in this paper can be applied to the turbulent drag reduction transport of long-distance pipelines for oil products to achieve the prediction of the drag reduction efficiency of polymer additives.

Structural Optimization Design of Electromagnetic Repulsion Mechanism Based on BP Neural Network and NSGA‐II

The long‐stroke electromagnetic repulsion mechanism is subjected to large stresses during the driving phase, and the repulsion disc is prone to fracture due to excessive vibration amplitude up and down. Therefore, it is necessary to reduce the peak stress and vibration amplitude of the repulsion disc, improve the mechanical life, and improve the driving performance without decreasing the motion displacement, but it is not possible to do so at present. To address this problem, this paper takes the ‘coil‐repulsion disc type’ electromagnetic repulsion mechanism as the research object, and builds a simulation model of electromagnetic repulsion mechanism with structural strength and driving performance by using finite element simulation software, and analyzes the effects of four structural parameters, namely, the height of the repulsion disc, the inner diameter of the repulsion disc, the radius of the circular table and the radius of the chamfer, on the displacement and stress distribution of the model. After that, 39 sets of samples were output by BBD experimental design method, which were introduced into the BP neural network prediction model and combined with NSGA‐II algorithm to optimize the structure of the electromagnetic repulsion mechanism. The simulation results show that with the repulsion disc height of 14 mm, the repulsion disc inner diameter of 14 mm, the radius of the circular table is 29.78 mm and the chamfer radius of 7.06 mm, the peak stress of the repulsion disc of the electromagnetic repulsion mechanism is reduced by 57.83%, the amplitude of the repulsion disc is reduced by 3.24 mm, the peak electromagnetic repulsion force is increased by 7.52 kN and speed at the end of 3 ms increased by 9.8%. Under the condition of satisfying the constraints and increasing the overall displacement, the structural strength is increased and the driving performance of the mechanism is ensured to be improved, providing reasonable mechanism parameters for the opening and closing requirements of the fast mechanical switch. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Effect of curing humidity on the deformation and mechanical properties of cemented paste backfill

ABSTRACT The influence of underground humidity on the mechanical performance of cemented paste backfill (CPB) in metal mines is a problem that cannot be ignored. To this end, this paper conducted the curing experiments of CPB samples under different curing humidity (60%, 80% and 95%). Physical properties such as moisture content and dry density as well as mechanical properties such as uniaxial compressive strength, cohesion and internal friction angle of the CPB samples were monitored during fixed curing time (3, 7, 14 and 28 days). TGA/DTG, XRD and SEM were used to analyse the microstructure of CPB samples under different curing humidity at 3 and 28 days, and a BP neural network prediction model of CPB strength was established based on the obtained data. The results show that the curing humidity has a great influence on the physical and mechanical properties of the paste, and the amount of hydration products and the change of pore structure are the main reasons that affect the properties. In order to ensure the safety and economy of the CPB structure, the influence of environmental humidity on the paste should be considered in the filling design.

Analysis of energy consumption prediction for office buildings based on GA-BP and BP algorithm

To gain building energy consumption information during the design phase, the variance analysis to identify significant factors affecting energy consumption in China cold-region office buildings are carried out in this study. Key factors are selected, and prediction models for energy consumption in cold-region office buildings are established using BP and GA-BP algorithms. Three prediction model evaluation indexes are introduced to evaluate the prediction accuracy of the models. The results show that the maximum RMSE of the BP neural network prediction model is 0.498, and the maximum MAPE is 0.797%. Furthermore, the GA algorithm is used to optimize the BP neural network, resulting in a prediction model with a maximum RMSE of 0.359 and a maximum MAPE of 0.289%. The prediction accuracy of the GA-BP algorithm is better than that of the BP algorithm.

New Data-Driven Method for In Situ Coalbed Methane Content Evolution: A BP Neural Network Prediction Model Optimized by Grey Relation Theory and Particle Swarm

New Data-Driven Method for In Situ Coalbed Methane Content Evolution: A BP Neural Network Prediction Model Optimized by Grey Relation Theory and Particle Swarm

Design and Experiment of Capacitive Rice Online Moisture Detection Device

To solve the problems of poor stability and low monitoring precision in the online detection of rice moisture in the drying tower, we designed an online detection device for rice moisture at the outlet of the drying tower. The structure of a tri-plate capacitor was adopted, and the electrostatic field of the tri-plate capacitor was simulated using COMSOL software. A central composite design of three factors and five levels was carried out with the thickness, spacing, and area of the plates as the influencing factors and the capacitance-specific sensitivity as the test index. This device was composed of a dynamic acquisition device and a detection system. The dynamic sampling device was found to achieve dynamic continuous sampling and static intermittent measurements of rice using a ten-shaped leaf plate structure. The hardware circuit of the inspection system with STM32F407ZGT6 as the main control chip was designed to realize stable communication between the master and slave computers. Additionally, an optimized BP neural network prediction model based on the genetic algorithm was established using the MATLAB software. Indoor static and dynamic verification tests were also carried out. The results showed that the optimal plate structure parameter combination includes a plate thickness of 1 mm, plate spacing of 100 mm, and relative area of 18,000.069 mm2 while satisfying the mechanical design and practical application needs of the device. The structure of the BP neural network was 2-90-1, the length of individual code in the genetic algorithm was 361, and the prediction model was trained 765 times to obtain a minimum MSE value of 1.9683 × 10−5, which was lower than that of the unoptimized BP neural network with an MSE of 7.1215 × 10−4. The mean relative error of the device was 1.44% under the static test and 2.103% under the dynamic test, which met the accuracy requirements for the design of the device.

Experimental and practical investigation of reinforcement mechanism on permeable polymer in loose area of drainage pipeline

Permeation grouting is one of the important methods of anti-seepage reinforcement when the drainage pipes crosses loose diseases caused by leakage. With the development of grouting materials, permeable polymer slurry is widely used in engineering practice. Due to its low viscosity, fast reaction and micro-expansion, the diffusion and reinforcement law of slurry in loose area of drainage pipeline is more complex. To better understand the effects of water head pressure, grouting pressure, sand particle size and clay content on the anti-seepage and reinforcement effect of permeable polymer grouting in loose area of drainage pipeline, several types of tests, including permeability and uniaxial compressive strength tests, were performed. Then, based on the model test results, a BP neural network prediction model for the anti-seepage reinforcement effect of permeable polymer grouting in loose area of drainage pipeline is constructed, and the research results are applied to the treatment project of loose area of drainage pipeline for verification. The results show that: 1) after grouting, the order of magnitude of permeability coefficient of the consolidated body is reduced to 10-7cm/s, the anti-seepage performance of the sand layer is greatly improved, and the grouting pressure is the main controlling factor affecting the anti-seepage performance of the consolidated body. 2) The compressive strength of the consolidated body is obvious different under different working conditions. The water head pressure is the main controlling factor affecting the strength of the consolidated body. 3) Due to the limitation of test conditions, the relative error between the predicted value of BP neural network model and the actual value is about 20%, which can meet the general prediction needs.

Research on the Construction Path of China's Zero-carbon Ports from the Perspective of Marine Carbon Sink Trading

At present, the international market has gradually carried out research on forecasting the trading price of carbon emissions with macroeconomics and energy market as the main influencing factors, supplemented by other factors such as policy factors. In order to speed up the construction of carbon accounting system, enrich the content of China's carbon pricing mechanism and improve the carbon emission trading market, this study constructs an index system from four aspects: macro-economy, energy price, climate environment and international market, and objectively screens out the important influencing factors of carbon trading price through Lasso regression, and uses BP neural network prediction model to predict carbon trading price. [7] We should focus on reducing the proportion of coal consumption by port enterprises and increasing other clean energy consumption. This result can provide basis and reference for the construction of zero-carbon ports and the construction of ocean-port carbon trading information platform in China.

Optimization of Aluminum Alloy Formwork Geometry Parameters Based on a PSO-BP Neural Network

To assist in addressing the problem where an aluminum alloy formwork (AAF) deforms more greatly under the action of lateral pressure and therefore does not meet the requirements of plaster-free engineering, we propose a method for determining the geometric parameters of this formwork based on a PSO algorithm and BP neural network with ABAQUS as the platform. The influence of six geometric parameters of the formwork on the maximum deflection value of the panel under the action of lateral pressure is studied using finite element analysis. The maximum deflection value of the panel is used as the index, and the influence of each factor is analyzed with an orthogonal test, and a set of optimal geometric parameters is obtained via extreme difference analysis and analysis of variance. The sample data are obtained via finite element simulation, and the PSO-BP neural network model is established using the six factors of the orthogonal test as input values and the maximum deflection of the panel as the output value, and the optimal geometric parameters are optimized using the PSO algorithm. The results indicate that the maximum deflection for the panel in the orthogonal scheme is 1.446 mm. The PSO-BP neural network prediction model demonstrates greater accuracy and a 31.74% reduction in running time compared to the BP neural network prediction model. The optimized PSO-BP neural network prediction model scheme reveals a maximum panel deflection of 1.296 mm, a 10.37% decrease compared to the orthogonal solution. These findings offer technical guidance and a foundation for optimizing AAF designs, presenting practical applications.

Laboratory investigation and BP neural network modeling on heat-activated oil shale semi-coke attributable to the mechanical properties of concrete

Concrete construction requires the consumption of large quantities of raw materials. If oil shale semi-coke can be used in concrete projects in a reasonable way, not only will it ease the shortage of raw materials for concrete, but it will also consume oil shale semi-coke and reduce its ecological impact. This investigation aims to study the feasibility of using semi-coke in concrete construction based on its mechanical properties. The chemical components and microstructures of four thermal activation temperature oil shale semi-cokes were first analyzed by X-Ray Fluorescence (XRF) and scanning electron microscope (SEM). Then, by replacing the cement with equal mass of semi-coke, the effect of heat-activated calcination temperature and the amount of semi-coke admixture on the mechanical strength of mortars sand and concrete was tested. Finally, a BP neural network was used to establish a strength prediction model for semi-coke concrete and the evolution of internal pores of semi-coke concrete was analyzed by nuclear magnetic resonance (NMR). The results show that semi-coke is mainly composed of SiO2 and Al2O3, which can be used as a concrete mineral admixture for volcanic ash reaction. The oil shale semi-coke is flaky with a porous surface, resulting in the semi-coke contributing more to the flexural strength of the mortar than to the compressive strength. At the calcination temperature of 500 ℃ and a semi-coke content of 15%, the concrete has the lowest porosity and the best mechanical properties, and is therefore recommended. The developed BP neural network prediction model has a high prediction level and can provide guidance for practical engineering. The increase in the amount of coke admixture leads to a continuous increase in the number of pores in the medium and large pore sizes inside the concrete, which is the main reason for the change in concrete strength due to semi-coke.

Prediction of global temperature based on the BP neural networks

The problem of global warming is getting worse as global temperature has risen year by year to an alarming level. This paper is first based on historical data on global temperatures, two prediction models were developed using Gray Prediction Method and BP Neural Network model, whose results show that the root mean square error test value of BP neural network is about 0.03 lower than that of the gray prediction method (0.1627).Thus, the BP neural network prediction model is proved effective, and then the model was analyzed by gray correlation theory, which found that carbon dioxide concentration, forest cover area and human population are affecting the global temperature, and the Gray correlation of the fisrt two factors is as high as 0.8, which makes the two the main factors. In the end the paper gave measures to contain global warming.

Prediction method of surface settlement of rectangular pipe jacking tunnel based on improved PSO-BP neural network

To provide theoretical support for the safety control of rectangular pipe jacking tunnels crossing an existing expressway, a method for predicting the surface settlement of a rectangular pipe jacking tunnel is proposed in this study. Therefore, based on the high approximation of the BP neural network to any function under the multiparameter input, the PSO-BP mixed prediction model of the ground subsidence of the ultrashallow buried large section rectangular pipe jacking tunnel is established by taking into account the adaptive mutation method, adopting the improved particle swarm optimization (IPSO) algorithm with adaptive inertia weight and mutation particles in the later stage to determine the optimal hyperparameters of the prediction model. Through the case study of an ultrashallow large cross-section rectangular pipe jacking tunnel, this algorithm is compared with the traditional algorithm and combined with field monitoring data for analysis and prediction. The prediction results show that compared with the traditional BP neural network prediction model, AWPSO-BP model and PWPSO-BP model, the improved PSO-BP mixed prediction model shows a more stable prediction effect when the change in surface subsidence is gentle and the concavity and convexity are large. The predicted subsidence value is close to the actual value, and the accuracy and robustness of the prediction are significantly improved.

Product quality prediction method in small sample data environment

The low degree of enterprise digitization and the existence of personalized customization and small batch production manufacturing modes lead to the characteristics of small samples and high dimensions of data collected by manufacturing enterprises. The product quality prediction method based on big data is prone to overfitting in the small sample and high dimensional data environment, and the model generalization performance is poor. In order to solve the above problems, this paper conducts research in two aspects of data augmentation and model optimization respectively. At the data augmentation level, a data generation model RVAE-CGAN is proposed, RVAE constrains the value space of GAN and eliminates the influence of outliers in the original data on the quality of generated samples. Using DBSCAN to cluster raw data to generate conditional vectors for guiding sample generation, and finally obtain high-quality generated samples. At the model optimization level, a product quality prediction model PPO-SVR is proposed. The PPO method is used to optimize the hyperparameters of the SVR model and improve the prediction effect of the model. RVAE-CGAN and PPO-SVR models are combined, and RVAE-CGAN is used to expand the number of samples to train PPO-SVR. The experimental results show that the product quality prediction model constructed based on RVAE-CGAN and PPO-SVR outperforms the BP neural network prediction model trained on mixed data. Therefore, the combination of data augmentation and model optimization proposed in this paper can significantly improve the prediction effect of the product quality prediction model in the small sample data environment.

Study on novel steel slag foam concrete pore structure and bp neural network prediction model

In this paper, a new type of steel slag foamed concrete was prepared. Its pore structure was analyzed by MATLAB image processing technique, and the influence of steel slag on the compressive strength of foamed concrete was investigated. In addition, a BP neural network prediction model for the pore structure of foam concrete was established. The research results show that when the content of steel slag is 30%, the pore structure of steel slag foamed concrete is appropriate. The compressive strength of steel slag foamed concrete increases with the decrease of the density and the proportion of irregular pores. With the density remaining unchanged, the compressive strength of foamed concrete increases with the increase of the ratio of round and small pores. The prediction errors of the BP neural network model in the average pore diameter and average pore roundness factor were less than 8.5% and 5%, respectively.This research can provide a theoretical basis for the production of steel slag foam concrete.