BP Neural Network Theory Research Articles

BP Neural Network-Enhanced System for Employment and Mental Health Support for College Students

This paper employs BP Neural Network (BPNN) theory to evaluate innovation and entrepreneurship education in universities. It utilizes students' evaluation indexes as input vectors and determines the number of hidden layer neurons. Experimental results serve as output vectors. The BPNN method proves reasonable and feasible for vocational education course evaluation, exhibiting a 14.96% higher accuracy than traditional genetic algorithms. The paper discusses the model, configuration, characteristics, training process, algorithm enhancement, and limitations of neural networks, followed by an introduction to genetic algorithms. Through analysis of principles, basic operations, and common operators, it establishes a theoretical foundation for subsequent discussions.

Financial Risk Early Warning Model for Listed Companies Using BP Neural Network and Rough Set Theory

Financial Risk Early Warning Model for Listed Companies Using BP Neural Network and Rough Set Theory

Research on the Influence of Heavy Pollution Environment on Hydrophobic Substances in Silicone Rubber Materials

Power load forecasting is very important for power dispatching. Accurate load forecasting is of great significance for saving energy, reducing generating costs, and improving social and economic benefits. In order to accurately predict the power load, based on BP neural network theory, combined with the advantages of Clementine in dealing with big data and preventing overfitting, a neural network prediction model for large data is constructed.

Retracted: Construction of College English Teaching Environment Assessment Model Based on BP Neural Network and Multiple Intelligence Theory.

[This retracts the article DOI: 10.1155/2022/9479755.].

Application of GA-BP in Displacement Force Inverse Analysis and Mechanical Parameter Inversion of Deep Foundation Pits

Aiming at the defects of various existing displacement inverse analysis methods, using the nonlinear mapping ability of neural network and the global random search ability of genetic algorithm, this paper proposes a displacement inverse analysis method based on optimized Genetic Algorithm- Back Propagation (GA-BP) for deep foundation pit support. The method changes the method that BP algorithm relies on the guidance of gradient information to adjust the network weights, but uses the characteristics of global search of genetic algorithm to find the most suitable network connection rights and network structure, etc. to achieve the purpose of optimization. Firstly, the deformation mechanism of deep foundation pit is analyzed, its failure mode is summarized, and the calculation method of lateral rock and soil pressure is sorted out according to the code. The theory and characteristics of BP neural network and genetic algorithm are discussed, and the method of using genetic algorithm to optimize BP neural network is proposed to improve the prediction accuracy. In view of the shortcomings of GA-BP neural network prediction model in training sample pretreatment and hidden layer structure design, the optimal normalization interval was determined by correlation coefficient regression analysis, and the analytical expression of the number of neurons in hidden layer was derived by statistical principle, and the value range of the optimal number of neurons in single hidden layer was proposed. Combined with the actual engineering, the mechanical parameters inversion and displacement force inverse analysis are performed using this method, and the results show that the optimized GA-BP has higher prediction accuracy compared with BP neural network and GA-BP, and the deviation of the displacement prediction value at each depth is kept within 0.2 mm, the absolute error interval width is 0.07 mm, and the maximum relative error is 1.35% at 4.0 m depth.

Analysis of the composition of ancient glassware based on grey correlation and principal component analysis

Power load forecasting is very important for power dispatching. Accurate load forecasting is of great significance for saving energy, reducing generating cost and improving social and economic benefits. In order to accurately predict the power load, based on BP neural network theory, combined with the advantages of Clementine in dealing with big data and preventing overfitting, a neural network prediction model for large data is constructed.

BP Neural Network-Based Evaluation Method for Enterprise Comprehensive Performance

Comprehensive performance evaluation is an important basis for improving the training effect of enterprise employees and the effective allocation of enterprise resources. Based on AHP and BP neural network theory, this paper constructs a comprehensive performance evaluation method for enterprises, AHP is used to calculate the weight of the index, and then the importance index is screened. The model proposes a conceptual model of comprehensive performance of manufacturing enterprises from the support layer, core layer, and promotion layer and constructs a manufacturing system from horizontal and vertical. The influencing factors of comprehensive performance solve the quantification problem of enterprise comprehensive performance evaluation and have obvious guiding value for the research on the integration mode and path of industrialization and industrialization of regional manufacturing enterprises. In the simulation process, the weight of each index in the evaluation system is first determined by the analytic hierarchy process; then the evaluation index membership score table is established, and fuzzy mathematics is used to calculate the expert’s score, so as to solve the problem caused by the intermediate value. The uncertainty caused by the jump is finally established by the analytic hierarchy process, and the neural network is used to simulate the sample. The experimental results show that by using AHP to collect training samples for neural network evaluation, the comprehensive performance evaluation system has good fitness and achieves the best comprehensive consideration of accuracy and training time when there are 17 hidden layer neurons. The maximum relative error is 1.64%, which is much lower than the general accuracy requirement of 5%, which effectively improves the performance and calculation accuracy of the network.

Highway Traffic Flow Prediction Model Construction Based on the Gray Theory and BP Neural Network.

The short-term traffic flow prediction and modeling of highways are the core content and important foundation of highway management decision-making support systems. It is of great significance to improving the level of highway management. Based on the macrodynamic traffic flow model, this article establishes a method for establishing a highway traffic flow prediction model based on the BP neural network theory and gray theory. We collected and carry out modeling and prediction of highway traffic flow data near a certain station. It is learned from the prediction results that the traffic flow prediction model based on the BP neural network and gray theory has a high degree of reliability.

Prediction of Building Energy Consumption Based on BP Neural Network

In order to solve the energy consumption hypothesis of large buildings, the energy consumption hypothesis based on the BP neural network is proposed. First, to study the system of statistical index of building energy consumption and the system of statistical reporting of energy consumption of civil construction. In addition, to establish reliable consumer authority control to ensure the security and management of the database. Second, based on an analysis of the mechanism by which the BP neural network operates, this article optimizes it and describes the structure of the neural network, which includes the number of network layers, the number of neurons in each layer, and the number of latent neuron layers. hidden neuron layers and hidden neurons. The maximum value method is used to normalize the input sample data; finally, the learning and training process of neural network is determined. Based on BP neural network theory, the energy consumption statistics platform and prediction system are established by using Delphi 6.0. These include functional modules such as basic building information management, building energy consumption information management, building energy consumption summary, energy presampling information management, and building energy consumption forecast; the collection of building energy consumption data is mainly completed by intelligent energy consumption monitoring sensor network system. Finally, the city’s building energy consumption information system conducts construction energy audits and analyzes the potential for energy savings. The results show that the hypothesis model determined by the BP neural network algorithm has an average error of 10.6% in predicting the construction energy consumption data, which is better than Matlab’s predicted result and the mean error is 12.6%. From this, it can be seen that the BP neural network algorithm can provide better predictions of building energy consumption.

Observation on Application Effect of Arterial Puncture and Catheterization under Guidance of Intelligent Medical Care Ultrasound in Clinical Anesthesia.

In clinical anesthesia and the rescue of critically ill patients, arterial puncture and catheterization are the most commonly chosen ways to establish central arterial access for patients. Invasive arterial puncture and catheterization facilitate the grasp of real-time vital sign information of patients during surgery, which strengthens patient monitoring during surgery and improves safety. However, the traditional method of arterial puncture and cannulation through palpation of the radial artery is often prone to complications related to mechanical injury, such as hemorrhage, hematoma, and accidental perforation of the artery. Studies have shown that ultrasound-guided radial artery puncture and cannulation can shorten the puncture cannulation time, reduce the incidence of complications related to puncture cannulation, and improve the success rate of puncture cannulation. In order to verify it, this paper uses the experimental group and the control group to conduct comparative experiments and uses the neural network method to evaluate the effects of the two methods. As a more mature method of artificial intelligence, BP neural network is widely used in a wide range of applications and has the characteristics of strong generalization ability and fast convergence, so we choose it as the base model. The specific work of this paper is as follows: (1) in-depth study of the relevant theory of BP neural network (BPNN), focusing on the structure of BPNN and the working principle of algorithm; the problems to be solved in the clinical anesthesia effect evaluation have laid a theoretical foundation for the establishment of an improved BPNN evaluation model in the following chapters. (2) introduce the basic principle of genetic neural network, analyze the benefits of combining genetic neural network and BPNN; introduce in detail the process of genetic algorithm to optimize the weights and thresholds of BPNN, and establish a GA-BP evaluation model. The test proves the feasibility and superiority of the model.

Construction of College English Teaching Environment Assessment Model Based on BP Neural Network and Multiple Intelligence Theory.

College English has almost always been a required course, and a college student's level of English proficiency is one of the factors used to assess their learning capacity. The quality of students' English learning is largely influenced by the level of English instruction provided in colleges. However, there are still a lot of issues with college English instruction today, the most glaring of which is that English instruction is being overly simplified, and that the methods, modes, and purposes of instruction are also very narrow. Due to this, it is challenging for most colleges and universities' English teaching levels to satisfy the requirements of high-level education. The PSO-BP neural network model, which optimizes the BP neural network (BPNN), is used in this study to build a high-precision and diversified English teaching evaluation model in order to address the aforementioned issues. According to the experimental findings, the PSO-BPNN algorithm has a relative error of just 0.29 percent and an average accuracy rate of 97.02 percent. Overall performance is superior to that of the conventional BPNN algorithm, and it is the most adaptable in terms of creating various evaluation modes.

Tourism Demand Forecasting Based on Grey Model and BP Neural Network

This article aims to explore a more suitable prediction method for tourism complex environment, to improve the accuracy of tourism prediction results and to explore the development law of China’s domestic tourism so as to better serve the domestic tourism management and tourism decision-making. This study uses grey system theory, BP neural network theory, and the combination model method to model and forecast tourism demand. Firstly, the GM (1, 1) model is established based on the introduction of grey theory. The regular data series are obtained through the transformation of irregular data series, and the prediction model is established. Secondly, in the structure algorithm of the BP neural network, the BP neural network model is established using the data series of travel time and the number of people. Then, combining BP neural network with the grey model, the grey neural network combination model is established to forecast the number of tourists. The prediction accuracy of the model is analyzed by the actual time series data of the number of tourists. Finally, the experimental analysis shows that the combination forecasting makes full use of the information provided by each forecasting model and obtains the combination forecasting model and the best forecasting result so as to improve the forecasting accuracy and reliability.

Research on Intelligent Prediction and Forecast Model for Construction Period of Transmission and Transformation Engineering Based on BP Neural Network

The construction period is one of the important control objectives of the construction and management of power transmission and transformation projects. If the construction period is not well controlled, it will lead to the failure of the project, cause disputes between all parties to the construction, and the economic and social benefits of the project cannot be ignored. Negative Effects. Therefore, it is of great significance to carry out the research on the construction period prediction method to improve the economic efficiency of the construction of power transmission and transformation projects. Based on the systematic analysis of the factors affecting the construction period of power transmission and transformation projects, this paper constructs an intelligent prediction model for the construction period of power transmission and transformation projects based on BP neural network theory, and verifies the validity and prediction accuracy of the model through empirical analysis. Reasonable control of construction period provides reference and reference.

Claim Amount Forecasting and Pricing of Automobile Insurance Based on the BP Neural Network

The BP neural network model is a hot issue in recent academic research, and it has been successfully applied to many other fields, but few researchers apply the BP neural network model to the field of automobile insurance. The main method that has been used in the prediction of the total claim amount in automobile insurance is the generalized linear model, where the BP neural network model could provide a different approach to estimate the total claim loss. This paper uses a genetic algorithm to optimize the structure of the BP neural network at first, and the calculation speed is significantly improved. At the same time, by considering the overfitting problem, an early stop method is introduced to avoid the overfitting problem. In the model, a three-layer BP neural network model, which includes the input layer, hidden layer, and output layer, is trained. With consideration of various factors, a total claim amount prediction model is established, and the trained BP neural network model is used to predict the total claim amount of automobile insurance based on the data of the training set. The results show that the accuracy of the prediction by using the BP neural network model to both the data of Shandong Province and to the data of six cities is over 95%. Then, the predicted total claim amount is used to calculate premiums for five cities in Shandong Province according to credibility theory. The results show that the average premium of the five cities is slightly higher than the actual claim amount of the city. The combination of BP neural network and credibility theory can perform accurate claim amount estimation and pricing for automobile insurance, which can effectively improve the current situation of the automobile insurance business and promote the development of insurance industry.

SOC estimation algorithm of power lithium battery based on AFSA‐BP neural network

The non-linear characteristic of power lithium battery restricts the establishment of accurate battery models. To overcome this problem and estimate the battery state of charge (SOC) more accurately, the artificial fish swarm algorithm-back propagation (AFSA-BP) neural network structure was designed based on AFSA and BP neural network theory. According to the test parameters of power lithium battery, the related mathematical model was established. The flow charts of optimising BP neural network with AFSA algorithm and estimating SOC value by AFSA-BP algorithm are given. The specific implementation steps are elaborated. Using the 48 V, 50 Ah lithium iron phosphate (LiFePO4) power battery as experimental object, through the periodic charging and discharging experiments and software simulation, the correctness, validity and accuracy of the application of AFSA-BP neural network in estimating SOC value of the power lithium battery are verified.

The Research on Bridge Engineering Risk Management and Assessment Model Based on BP Neural Network

Bridge engineering risk research is an important part of bridge engineering management. This paper establishes the risk indicators system of bridge project, and then introduces BP neural network theory into bridge risk evaluation. By using the neural network toolbox of MATLAB, a risk assessment model of BP neural network is bulit. The validity and feasiblility of the risk evaluation model is verified by a practical example.

Theoretical and experimental studies on the optimization of finger seal

For the finger seal, the impact of structural parameters on its performance is complicated, and it is difficult and contradictory to lower its leakage and wear synchronously. Therefore, the problems relating to decision-making of optimal results and selection of reference variables still confuse the optimization of finger seal. In this paper, the performance (leakage and wear) of finger seal are calculated with finite element simulation, and a sensitivity analysis is carried out based on grey correlation method to obtain the influence degrees of structural parameters on the performance of finger seal; then a multi-objective optimization model is proposed by the combination of genetic algorithm, BP neural network and Nash equilibrium game theory; finally, the experiments of finger seals with various optimal structures are carried out to verify the theory and method established in this paper. The theoretical and experimental results indicate that the finger seal optimized with the model proposed in this paper has a better synthetical performance, which proves its feasibility. The theory and method established in this paper can be applied to enhance the synthetical performance of finger seals effectively.

Intelligent teaching ability of contemporary college talents based on BP neural network and fuzzy mathematical model

In the evaluation of traditional college talents’ teaching ability, the importance of evaluation indicators lacks evaluation, and the evaluation results are relatively random. In order to improve the evaluation efficiency of university scientific research talents, this study combines BP neural network and fuzzy mathematical theory to build an evaluation model. Combining the talent training process and ability requirements of colleges and universities, a secondary index system is proposed, and the weight of the evaluation index is determined by combining data collection. This paper first normalizes the samples, determines the training and test samples, and then uses trial and error to determine the number of hidden layer neurons. Then use fuzzy mathematics theory to construct fuzzy similarity matrix to describe the fuzzy relationship between factor domain and judgement domain. Calculate membership to get comprehensive evaluation results. Finally, this paper uses statistical methods to draw the results into statistical charts and combines the simulation results to obtain performance comparison results. The feasibility of the model is verified by experimental research, and the model can be applied to practice, and can provide theoretical reference for subsequent related research.

Risk early warning safety model for sports events based on back propagation neural network machine learning

In order to ensure the smooth progress of sports events and reduce the possibility of risk accidents, a risk early warning safety model of sports events based on BP neural network and fuzzy theory is established, which has great advantages. The establishment of the model is divided into five steps: (1) using BP neural network as the basic network structure to obtain the fuzzy data; (2) using fuzzy theory to form the eigenvalue matrix; (3) optimizing the evaluation of the early waiting warning data; (4) determining the model; (5) determining the comprehensive numerical value of sports events risk according to the weight and threshold range of the obtained fuzzy optimized BP neural network, alarm based on relevant indicators. In the empirical analysis, the appropriate sample data was selected, the BP neural network topology structure was construct, the simulation training was carried out, the training error results of the input neural network nodes were calculated. Bying comparing the output data of the neural network with the theoretical data it is found that this risk early warning safety model has certain validity and reliability, and can achieve better early warning effect.

Soft neural network-based block chain risk estimation

Financial risk refers to the uncertainty caused by the change of the economic and financial conditions. As a kind of economic phenomenon, the financial risk is objective and can not be eliminated. At present, there are still some imperfect aspects in the research of financial risk assessment. In order to achieve the purpose of comprehensive evaluation of financial risks, the paper analyses the methodology of soft computing and neural networks. The basic function of financial risk monitoring and evaluation system is to forecast the trend of financial activities and risk status, and this is also the fundamental function and objectives of the assessment system. We use BP neural network theory to establish the logistics finance risk evaluation model, using BP neural network structure and training principles to train sample data. The soft computing method is based on the factors of uncertainty and irrationality, which breaks through the limitation of traditional hard computing. There is a consistency between the fuzzy thinking principle of soft computing method and the attribute and structure of the objective world and therefore, soft computing can be used in field of financial risk assessment.