BP Neural Network Prediction Model Research Articles

Construction and Application of “Active Prediction-Passive Warning” Joint Impact Ground Pressure Resilience Prevention System: Take the Kuan Gou Coal Mine as an Example

Abstract With the increasing depth and intensity of coal mining, the impact on ground pressure has become one of the main disasters facing mining, seriously threatening mine safety. Introducing the concept of toughness urban design, building a joint toughness prevention and control system based on active prediction and analysis of the impact pressure risk at the back mining face according to the geological deposit conditions and mining technology conditions and passive warning using monitoring data to explore the impact precursor characteristics is an important basis for impact pressure management and has important engineering significance to ensure the safe back mining. In this paper, firstly, the whole working face is divided into small unit areas, and the BP neural network prediction model is constructed to predict and analyze each small unit separately, and the distribution of impact ground pressure hazard level in different areas of the working face is derived. Next, a FLAC numerical model was established to analyze the stress distribution and migration characteristics at different retrieval distances of the working face and to explore the main distribution areas of impact hazard. Finally, the trend method, critical value method, and dynamic rate of change method were applied to determine the early warning indicators of impact ground pressure in the Kuan Gou coal mine, establish a comprehensive early warning method of impact ground pressure applicable to the Kuan Gou coal mine, and carry out field application with good effect. The findings of this paper have good scientific significance and reference value for promoting impact hazard analysis and early warning in mines with similar geological conditions and mining technology conditions in China.

Performance control study of interleaved meltblown non-woven materials based on statistical analysis and predictive modeling.

Meltblown nonwoven materials have gained attention due to their excellent filtration performance. The research on the performance of the intercalation meltblown preparation process is complex and a current research focus in the field of chemical production. Based on data related to intercalated and unintercalated meltblown materials under given process conditions, a product performance prediction model of intercalated meltblown materials was established under different process parameters (receiving distance, hot air velocity). The structural variables (thickness, porosity, and compressive resilience), the change in product performance, and the relationship between structural variables and product performance (filtration resistance, efficiency, air permeability) after intercalation were studied. Multiple regression analysis was used to analyze the structural variables, and evaluation of the regression results were made using R2, MSE, SSR, and SST. A BP neural network prediction model for product performance was established. The BP neural network model was used to find the maximum filtration efficiency. The study provides theoretical support for regulating product performance by solving the maximum filtration efficiency using BP neural network model.

Research on Stock Price Prediction of BP Neural Network Based on Factor Analysis

In this thesis, the real estate industry is focused on, the financial data of 27 listed real estate companies in China is chosen, and the method of factor analysis is used to carry out the dimensionality reduction processing for the collected 20 variables. Finally, six main factors are extracted. Besides, the stock prices before and after the factor analysis are predicted and studied respectively based on the BP neural network prediction model. In line with the results, the better prediction accuracy for the BP neural network model established with the main factor determined by factor analysis as the independent variable and the stock price as the dependent variable is embodied. In contrast with the research carried out by other scholars, the BP neural network is established from more dimensions, so the prediction accuracy is greatly enhanced.

Portfolio investment decision model based on price forecasting model and risk assessment model

Investors often use gold and Bitcoin to reduce the risk of financial investments by combining them due to their low correlation and different dynamic correlation. Therefore, it is essential to study how a combination of the two can be invested to maximize returns. In this paper, we combine the BP neural network model and the gray correlation analysis model from the future unpredictability and risk quantification of portfolio investment. We want to use the model to construct a price prediction model and risk assessment model to build a model for portfolio investment decisions based on past-day price flow only. To be specific, this paper first builds a price prediction model based on a BP neural network prediction model. At the same time, the prediction needs to be performed in a cycle, and the training set of the neural training model is increased each time. Each prediction is recorded for the next day's data and aggregated to obtain the daily price flow prediction. Next, this paper builds a risk assessment model through gray correlation analysis. In order to measure the riskiness of the daily investment, this paper selects three indicators, namely, forecast price accuracy, recent price trend, and price volatility. It uses gray correlation analysis to score them quantitatively and then gives the daily investment risk assessment results. Finally, a sensitivity analysis was performed to determine the model's sensitivity to transaction costs by varying the transaction costs of gold and bitcoin. It was found that as the transaction costs become more extensive, the number of transactions becomes smaller and the total assets acquired become smaller.

Prediction of Compressive Strength of Concrete Using the Spearman and PCA-Based BP Neural Network

Using real-time production data of concrete to predict its 28-day compressive strength has great significance for improving the engineering structure quality and overcoming the shortage of the traditional tests long period of concrete compressive strength. The current research has the shortcomings such as insufficient prediction accuracy, inadequate matching between data characteristics and model characteristics, and redundant input parameter information. This paper proposes a BP neural network prediction model optimized by Spearman and PCA. The model first uses Spearman method to reduce the number of the input variables by eliminating variables that have a low correlation with the compressive strength and then uses PCA to eliminate the correlation between material-related variables. Following this, the new uncorrelated input variables optimized by Spearman and PCA are input to the BP neural network-based model to predict the compressive strength of concrete. The results showed that it yielded the mean absolute percentage error (MAPE) of 2.78% and the root mean-squared error (RMSE) of 1.66 MPa, far lower than the error of 4.82% and 2.92 MPa obtained by the nonoptimized BP neural network, respectively. The proposed model fully exploits real-time monitoring data from the concrete mixing station, and its results are close to those of traditional tests. It has great practical significance to guide the concrete production and construction, shorten the production cycle, and reduce the project cost.

Asset allocation strategy based on CRITIC method and BP neural network

Investment portfolio optimization has become more and more mature after more than 30 years of development in China's capital market. For ordinary investors, how to reasonably match holding assets to ensure a certain level of returns and reduce investment risks is an important issue. Using 100 securities in the China Securities Dividend Index as index samples, firstly, a BP neural network prediction model is established for prediction, then the CRITIC method is used to evaluate and score the 100 securities, and finally a multi-objective planning model is established for investment portfolio. The results show that: solving the optimal solution of the multi-objective programming model to obtain the investment portfolio and obtaining the investment portfolio strategy; using the appropriate prediction model, evaluation model and establishing the multi-objective programming model can not only reduce investment risk for ordinary investors, but also reduce investment risk. received a certain level of income.

Short-term photovoltaic power generation power prediction method based on SOA-BP neural network

The size of photovoltaic power generation has a certain uncertainty, which affects the quality of photovoltaic power generation, and affects the safety and stable operation of the power grid. By predicting the photovoltaic power generation, this problem can be better solved. A photovoltaic power generation power prediction method combining SOA (Seagull optimization algorithm) and BP neural network is proposed. The convergence speed of BP neural network prediction model is relatively slow, and it is easy to fall into local optimum. Optimizing its weights and thresholds through SOA can improve these situations. Through experiments, Through experimental comparison, it is found that the BP neural network method optimized by seagull algorithm has higher accuracy in predicting photovoltaic power generation.

Starch-based aerogel prepared by freeze-drying: establishing a BP neural network prediction model

Starch-based aerogel prepared by freeze-drying: establishing a BP neural network prediction model

Research on Dam Deformation Prediction Model Based on Optimized SVM

Although constructing a dam can bring significant economic and social benefits to a region, it can be catastrophic for the population living downstream when it breaks. Given the dynamic and nonlinear characteristics of dam deformation, the traditional dam prediction model has been unable to meet the actual engineering demands. Consequently, this paper advocates for a novel method to solve this issue. The proposed method is based on the optimization of improved chicken swarm (ICSO) and support vector machine (SVM). To begin with, the mean square error is used as the objective function, and then, we apply the improved chicken swarm algorithm to iterate continuously, and finally, the optimal SVM parameters are obtained. Through the modeling and simulation experiments of a nonlinear system, the validity of the improved chicken swarm algorithm to optimize an SVM model has been verified. Based on the horizontal displacement monitoring data of FengMan Dam, this paper analyzed the influencing factors of horizontal displacement. According to the results, three prediction models have been established, respectively: the SVM prediction model optimized by the improved chicken swarm algorithm, the SVM prediction model optimized by the basic chicken swarm algorithm and the BP neural network prediction model optimized by the genetic algorithm. The obtained results from the experiment authenticate the validity and superiority of the proposed method.

Prediction of Enterprise Economy by Network Communication Business Based on BP Neural Network in Big Data Security Environment

From the environmental and security status quo faced by big data, although big data is unstructured or more difficult to filter and analyze, it does not mean that big data is necessarily more secure. In order to be invincible in the fierce market competition, an enterprise must conduct an in-depth understanding and investigation of the rapid changes in the market and economic development. The current era is already the Internet era. The arrival of the era of network communications has greatly facilitated people’s lives. However, how to seize the opportunities in the era of network communication and make economic predictions based on actual situations are particularly important for enterprises. Therefore, we are required to make reasonable economic predictions of network communication services in order to seize opportunities and meet challenges. By analyzing the development and application of artificial neural network (NN), this article briefly introduces its development and principles. Based on the reality of a network communication industrial enterprise, it uses data modeling and comparative analysis to establish a logistic regression model, a decision tree model, and a BP NN. The model compares the customer types and data predictions of the enterprise’s data traffic under the three models. From the results of the model analysis, it can be seen that in the analysis of the three models, the ROC curve analysis, the BP NN predicts that the cumulative hit rate in the ROC curve is wider. More users who can handle traffic services will be covered. In the cumulative customer lift analysis, under the first set of data (that is, when the depth is 20), the cumulative depths of the decision tree model, logic analysis model, and BP neural system prediction model are 2.5, 3.4, and 3.6, respectively. The BP neural system prediction model has the highest value of cumulative depth. In the cumulative capture response percentage analysis, the cumulative capture response percentage from low to low is the decision tree model and logic analysis. From BP NN prediction model, we can draw the conclusions. The network system can play a good role in forecasting and help enterprise managers make economic decisions.

Research on Securities Portfolio Model Based on Genetic Optimization Neural Network

Portfolio is an investment management concept different from individual asset management. This consideration leads to an interesting result, that is, investors should buy a variety of securities at the same time instead of one kind of securities for diversified investment. Aiming at the limitations of BPNN (BP neural network) in traditional artificial neural network and its shortcomings such as many iterations, low convergence accuracy, and poor generalization, a portfolio method based on GA_BPNN (Genetic Optimization Neural Network) was proposed. The setting of GA (genetic algorithm) parameters and BPNN parameters are discussed in detail, and the implementation steps of genetic BP algorithm are described. The results show that the evaluation indexes of GA_BPNN prediction model are obviously better than those of the comparison prediction model, with the coincidence rate of 77.96% and the average absolute error of 12.451. The combination of GA and BPNN can effectively solve this problem. The simulation results of optimizing securities portfolio show that its optimization scheme is better than quadratic programming method, and this method is more correct, efficient, and practical.

Prediction of Information Talent Demand Based on the Grayscale Prediction Model and the BP Neural Network

With the increasing popularity of the Internet technology, people are now increasingly accustomed to obtaining information or help through the Internet. Meanwhile, the great development of the information service industry has led to the explosive growth of the demand for information service talents. In recent years, many information service talent demand reports have been released in China, and it has an important guiding significance for information service industry planning. However, there are three problems with the information service industry talent demand reports at present. First, the relevance and support of talent demand analysis and forecast to information service industry planning need to be clarified. Second, the coordination and cooperation of information service personnel demand report preparation need to be improved. The third is the wider application of scientific and reasonable information service personnel demand forecasting models. In the future, we need to develop and use more reasonable information service personnel demand forecasting models and improve the quality of information service personnel demand reports. At the same time, the supporting role of the information service industry in scientific planning needs to be strengthened continuously. Therefore, information service industry talent demand forecasting is of great significance. In this paper, a prediction model of information service talent demand is established by using gray system theory. For the deviations of the GM (1, 1) model, a combined GM (1, 1)-BP neural network prediction model is proposed. The simulation results show that the prediction results of the prediction model in this paper are satisfactory. Therefore, the GM (1, 1)-BP model proposed in this paper can be used as a reference for government decision-making and information service personnel training.

Evaluation of Corrosion Residual Life Prediction Methods for Metal Pipelines.

The analysis of the basic characteristics of various research methods is highly needed to predict the residual life of the pipeline accurately, help managers understand the operational risks, and provide a reference for developing pipeline transportation and maintenance inspection plans and anti-corrosion measures. Based on a comprehensive investigation of the existing research on the residual life of the pipeline, this paper finds that the current mainstream life prediction method, based on historical statistical data, has the shortcomings of inconsistent modeling methods, inconsistent basic data, and a lack of comparative evaluation among methods. Moreover, considering the in-depth study of BP neural network modeling, grey theory modeling, time series modeling, and exponential smoothing modeling, optimal prediction models using different methods based on the same historical data are established. These optimal modeling methods are discussed, and the feasible modeling path for the accurate prediction of the pipeline’s residual life is given by comparing the prediction accuracy of each model. In addition, the findings serve as a guide for developing an anti-corrosion strategy by highlighting the contribution of the prediction results of the residual life to pipeline decision-making. By comparison, it is found that the accuracy of the four prediction models is as follows: the grey theory prediction model, the exponential smoothing prediction model, the BP neural network prediction model, and the time series prediction model, from high to low, respectively.

Prediction Model for Driver Reaction Time Based on PSO-BP Neural Network Model

A critical element of accident reconstruction technology is the driver reaction time, which is also a key aspect of evaluating driver takeover time in autonomous driving. There are various factors affecting driver reaction time, including driver psychological factors, nondriving tasks, external environment, and other reasons. It is necessary to record and predict the driver reaction time to support the takeover time in the degraded takeover study because this paper is designed to conduct a degraded takeover study in the human-machine codriving stage with safety of the intended functionality. Therefore, a model based on PSO-BP neural network algorithm to predict driver reaction time is developed. A wavelet transform algorithm is used to denoise the signal first in order to improve the convergence speed and prediction accuracy of the model. Meanwhile, the BP neural network prediction model based on the PSO is established to optimize the weights and thresholds of the BP neural network to achieve the prediction of the driver reaction time. A total of six main feature parameters of driver’s HRV in the time and frequency domains were selected as input indicators and substituted into the input signal of PSO-BP neural network model for training and testing. The prediction results obtained from the PSO-BP neural network model were compared with that of the BP neural network prediction, and it demonstrated that the prediction results obtained in this paper have smaller error values, verifying the reasonableness and validity of the model.

The Prediction of Public Risk Perception by Internal Characteristics and External Environment: Machine Learning on Big Data.

Presently, the public’s perception of risk in terms of topical social issues is mainly measured quantitively using a psychological scale, but this approach is not accurate enough for everyday data. In this paper, we explored the ways in which public risk perception can be more accurately predicted in the era of big data. We obtained internal characteristics and external environment predictor variables through a literature review, and then built our prediction model using the machine learning of a BP neural network via three steps: the calculation of the node number of the implication level, a performance test of the BP neural network, and the computation of the weight of every input node. Taking the public risk perception of the Sino–US trade friction and the COVID-19 pandemic in China as research cases, we found that, according to our tests, the node number of the implication level was 15 in terms of the Sino–US trade friction and 14 in terms of the COVID-19 pandemic. Following this, machine learning was conducted, through which we found that the R2 of the BP neural network prediction model was 0.88651 and 0.87125, respectively, for the two cases, which accurately predicted the public’s risk perception of the data on a certain day, and simultaneously obtained the weight of every predictor variable in each case. In this paper, we provide comments and suggestions for building a model to predict the public’s perception of topical issues.

A comprehensive evaluation of forest value based on carbon sequestration stock prediction

The increasing greenhouse effect globally, the excessive deforestation of forests and the weakening of forest carbon storage capacity have brought about drastic changes in the global climate and posed a great threat to the biosphere. Therefore, the issue of "making efficient use of forest carbon storage capacity and diversified value of forests" has attracted our attention. To better analyze the problem of carbon sequestration and forest value and formulate a reasonable forest management plan, this article established two models: the "grey system model" and "BP neural network prediction model." The two models used China's forest inventory data in the past 40 years to predict that the average value of total forest carbon sequestration in China in a hundred years is 189.37×108t. Aiming at the problem of forest value evaluation, this article apply "grey correlation analysis method," "main component analysis model," and "fuzzy comprehensive evaluation model." This article considers the value of forests from two aspects: ecological value and economic value. In terms of ecological value: this article found that forests have various values such as "carbon sequestration, water conservation, soil conservation, and biodiversity maintenance"; Most forests have a "U-shaped transition period" in carbon sequestration capacity. "Tree age and tree composition structure" are the main factors affecting carbon sequestration capacity. In terms of economic value, this article found that "increasing the proportion of young trees and suitable for the production of forest products" is a good way to increase the economic value of forests. In addition, the research shows that there are obvious differences in the degree of carbon sequestration capacity and economic value of forests in different climate areas such as "subtropical zone, warm temperate zone, and middle temperate zone.".

An Optimized Neural Network Prediction Model for Reservoir Porosity Based on Improved Shuffled Frog Leaping Algorithm

Efficient and accurate porosity prediction is essential for the fine description of reservoirs, for which an optimized BP neural network (BPNN) prediction model is proposed. Aiming at the problem that the BPNN is sensitive to initialization and converges to local optimum easily, an improved shuffled frog leaping algorithm (ISFLA) is proposed based on roulette and genetic coding. Firstly, a roulette mechanism is introduced to improve the selection probability of elite individuals, thus enhancing the global optimization ability. Secondly, a genetic coding method is carried out by making full use of effective information such as the global and local optimal solutions and the boundary values of subgroups. Subsequently, the ISFLA algorithm is verified on 12 benchmark functions and compared with four intelligent optimization algorithms, and experimental results show its good optimization performance. Finally, the ISFLA algorithm is applied to the optimization of initial weights and thresholds of the BPNN, and a new model named ISFLA_BP is proposed to study the porosity prediction problem. The logging data is preprocessed by grey correlation analysis and deviation normalization, and then the effective prediction of porosity is achieved by natural gamma, density and other relevant parameters. The performance of ISFLA_BP model is compared with the standard three-layer BPNN and four BPNN parameter optimization methods based on swarm intelligence algorithms. Experimental results show that the proposed model has higher training accuracy, stability and faster convergence speed, with a mean square error of 0.02, and its prediction accuracy for porosity is higher than that of the other five methods.

Experimental research on weathercock interference characteristics of weathercock airspeed tube

Pitot tube is one of the most important airborne devices on the aircraft. The airspeed tube can not only provide the pilot with the airspeed of the aircraft, but also the rate of climb of the aircraft. In this paper, the wind tunnel test method is used to test the weathercock type airspeed tube used on a domestic supersonic tailless layout aircraft and it obtains the airspeed tube's angle of attack and sideslip angle weathercock interference characteristics and total static pressure aerodynamic characteristics. In this process, we find that the sideslip angle begins to offset with the angle of attack increasing and the offset is about -1° when the airspeed exceeds 1.5 Ma. Subsequently, CFD calculations are used to compare and supplement the wind vane characteristics obtained from the wind tunnel test. It is found that the pressure data collected by the airspeed tube has a certain error due to the interference characteristics of the wind vane. Finally, the pressure error is corrected by BP neural network prediction model. It provides a reference to calibrating air-data-sensors for researchers.

An Intelligent Optimization for Building Design Based on BP Neural Network and SPEA-II Multiobjective Algorithm.

With the continuous development of the field of building optimization, more and more optimization methods have sprung up, among which there are many kinds of intelligent optimization algorithms. This kind of intelligent optimization algorithm usually relies on the traditional building performance simulation method to obtain the building performance index for optimization. However, intelligent optimization algorithms generally require large-scale calculations. At the same time, the time required for building performance simulation is often limited by the complexity of building models and the configuration of computers, which leads to a long time for performance optimization, which cannot give efficient and accurate feedback to designers in engineering. Building performance optimization methods based on intelligent optimization algorithms are mainly used in scientific research and are difficult to put into practical projects. Therefore, this paper builds an accurate and efficient platform for building performance prediction and optimization to help designers make decisions combined with BP neural network and the SPEA-II multiobjective optimization algorithm. Besides, the optimization results of the case are quantitatively and qualitatively analyzed and presented in visual form based on the BP neural network prediction model. Quantitative analysis includes the evolution process of solution set, convergence process, and comprehensive quality evaluation of solution set. Qualitative analysis includes Pareto frontier and optimal architectural scheme analysis. Finally, the conclusion shows that the platform prediction and optimization can give accurate and reliable optimal solution, and the optimal building scheme is reasonable and has high engineering application value.

Fuzzy Random Prediction Model of Frost Heave Characteristics of Horizontal Frozen Metro Contact Channel in Coastal Area

The frost heave characteristics of artificial frozen soil are very important information for underground freezing engineering. It is found that both the frost heaving force and rate of the same soil layer increase with the decrease of freezing temperature. In addition, due to the comprehensive influence of freezing temperature, natural water content, dry density, and tidal flow peak value, the frost heave characteristics of different soil samples are evidently uncertain. With the aim of improving the deficiency of traditional BP neural network algorithms in solving fuzzy random engineering problems, random factor and mean square error between layers are used to modify the evaluation function of the network model. On this basis, taking tidal flow peak value, freezing temperature, natural water content, and dry density as inputs, the frost heaving force and rate of frozen soil as output values, and setting the number of hidden layer elements as 4, an improved fuzzy random BP network prediction model for frost heave characteristics was established. The new network prediction model has a smaller weight and bias, and the response tends to be smoother than the traditional one, which greatly reduces the overfitting phenomenon. The engineering example shows that the improved BP neural network prediction model can make the predicted value of frost heaving force and rate basically coincide with the measured value after effective training, and the error is controlled within 8%. Therefore, the prediction model can be used as an effective tool to predict frost heaving characteristics in Nantong metro freezing construction, and the corresponding model and method can also be extended to similar engineering cases.