BP Neural Network Algorithm Research Articles

Research on BP neural network model-based data mining technique in KOL identification

Abstract. Accompanied by the development of artificial intelligence industry, to play the data value of web crawler technology has been the focus of research in the field of computer network and data science, and the data mining technology based on artificial neural network intelligent algorithm is widely used. In view of this, this paper takes the BP neural network data mining technology, which has excellent nonlinear mapping capability, parallel processing capability and fault tolerance and is widely used, as the basis, and integrates the methods and ideas of data mining into the mining of data laws in the field of KOL identification of key opinion leaders, with a view to finding valuable intrinsic laws and relationships between the mining of web crawler technology and the identification of KOL features. The research content of this paper mainly includes two aspects of research work, the design of high-performance data mining technology and the actual work in the field of KOL recognition. On the one hand, this paper comprehensively describes the basic theory and methods of data mining, and focuses on the in-depth analysis and elaboration of BP neural network-based data mining technology on the basis of understanding and analyzing a variety of data mining technologies. On the other hand, this paper aims to solve the problem of bias in the prediction of traditional KOL model and design the experimental method of KOL recognition by BP neural network algorithm.

Multi-objective optimization of switched reluctance motor driving electric vehicles based on vehicle operating condition

To improve the comprehensive performance of the switched reluctance motor (SRM) for electric vehicles (EVs) under actual driving conditions, a novel geometrical optimization method based on the time-varying phase current characteristics and the comprehensive indicator with justified weight coefficients is proposed in this paper. First, based on the established dynamic models of EV and SRM, the actual operating phase currents of the motor working under the New European Driving Cycle (NEDC) are extracted and classified to develop the representative optimization conditions. Then, considering the requirements of EV, five indicators are defined to evaluate the comprehensive performance of SRM, and a new multi-objective synchronization optimization function is proposed with five weight coefficients obtained by the statistical analysis method for the sample data of the indicators. Furthermore, the geometrical optimization for a four-phase 8/6 SRM is carried out by the combination of Particle Swarm Optimization (PSO) and BP neural network algorithms, after analyzing the effects of key geometrical parameters on the indicators. Finally, the work performance of the optimized SRM is evaluated and compared with that of the initial prototype, and the results show that the optimized SRM with the proposed multi-objective synchronization optimization method can improve the comprehensive indicator by 59.59%, and can significantly enhance the vehicle actual working economy by 50.74% under NEDC cycle.

Research on tourism demand forecast based on BP neural network -- Taking the number of tourists in Mulan Scenic spot in Huangpi District for example

With the development of the economy and society, the public's demand for tourism is getting higher and higher, followed by the environmental carrying capacity of the destination, the bearing capacity of traffic reception facilities, and the psychological carrying capacity of tourism residents being affected by different degrees. This paper took the analysis of the number of tourists in Mulan Scenic Spot in Huangpi District for the May Day holiday as an example, combined with the BP neural network algorithm, forecast the tourism demand, and put forward the local government's suggestions on holiday tourism management based on the data research.

Precision and uncertainty in natural gas calorific value estimation: advanced combinatorial predictive models for complex pipeline systems

Abstract In recent years, natural gas pipelines have been characterized by multiple intake points, the mixing of different gas sources, and significant variations in gas quality. The existing volumetric measurement system is no longer suitable for the development and operation of natural gas pipelines in China. To ensure accurate and equitable implementation of natural gas measurement, there is a gradual shift towards energy measurement. However, due to numerous measurement interfaces, installing chromatographs at all measurement stations would lead to substantial investment costs. Therefore, predicting the calorific value of natural gas is a key technology for energy measurement. In view of the issues existing in the current methods of obtaining natural gas calorific value, different prediction models of natural gas calorific value are constructed. According to the influencing factors of natural gas calorific value, the uncertainty evaluation is carried out, and the accuracy of different calorific value prediction models is analyzed by case data. The maximum error of regression equation (RE), response surface analysis (RSA), BP neural network and genetic algorithm (GA) prediction model is 2.29%, 0.43%, 0.59% and 0.45% respectively. However, the combined calorific value (CCV)prediction model is 0.41%. The results indicate that the predicted value calculated by the CCV prediction model is closer to the actual value and has higher prediction performance, which provides a new method for the calorific value prediction and measurement management of natural gas.

Prediction of Shale Gas Well Productivity Based on a Cuckoo-Optimized Neural Network

Current shale gas well production capacity predictions primarily rely on analytical and numerical simulation methods, which necessitate extensive calculations and manual parameter tuning and produce lowly accurate predictions. Although employing neural networks yields highly accurate predictions, they can easily fall into local optima. This paper suggests a new way to use Cuckoo Search (CS)-optimized neural networks to make shale gas well production capacity predictions more accurate and to solve the problem of local optima. It aims to assist engineers in devising more effective development plans and production strategies, optimizing resource allocation, and reducing risk. The method first analyzes the factors influencing the production capacity of shale gas wells in a block located in western China through correlation coefficients. It identifies the main factors affecting the gas test absolute open flow as organic carbon content, small-layer passage rate, fracture pressure, acid volume, pump-in fluid volume, brittle mineral content in the rock, and rock density. Subsequently, we used the CS algorithm to conduct the global training of the neural network, avoiding the problem of local optima, and established a neural network model for predicting shale gas well production capacity optimized by the CS algorithm. A comparative analysis with other relevant methods demonstrates that the CS-optimized neural network model can accurately predict production capacity, enabling a more rational and effective exploitation of shale gas resources, which lower development costs and increase the economic returns of oil and gas fields. Compared to numerical simulation, SVM, and BP neural network algorithms, the CS-optimized BP neural network (CS-BP) exhibits significantly lower prediction error. Its correlation coefficient between predicted and actual values reaches as high as 0.9924. Verification experiments conducted on another shale gas well also demonstrate that, in comparison to the BP neural network algorithm, CS-BP offers superior prediction performance, with model validation showing a prediction error of only 0.05. This study can facilitate more rational and efficient exploitation of shale gas resources, reduce development costs, and enhance the economic benefits of oil and gas fields.

Application of Seagull Optimization Algorithm-BP Neural Network in Oil-Water Two-Phase Flow Pattern Forecasting

With the ongoing increase in global energy demand, the significance of innovations in oil exploration and development technologies is rising, especially in relation to the development of unconventional reservoirs. The application of horizontal wells is becoming increasingly important in this particular situation. However, accurately monitoring and analyzing fluids in horizontal wells remains challenging due to the complex and fluctuating flow patterns of oil-water two-phase flow within the wellbore. Several elements, including well slope angle, flow rate, and water content, are involved. This study aimed to explore and develop an effective method for forecasting flow patterns, improving the precision of the dynamic monitoring of oil-water two-phase flow in horizontal wells. By analyzing the flow patterns in different experimental conditions, a predictive model using the SOA-BP neural network was developed, providing a scientific basis for dynamic monitoring in actual production scenarios. Initially, the simulated experiment for oil-water two-phase flow was carried out at room temperature and pressure utilizing a multiphase flow simulator. An optically transparent wellbore, with a diameter comparable to that of a real downhole well, was utilized, and No. 10 industrial white oil and tap water were employed as the experimental fluids. The experiment considered multiple contributing factors, including different well deviation, total flow, and water cut. The flow characteristics of oil and water were observed via visual monitoring and high-definition video, followed by detailed analysis. After collecting the experimental data, flow regimes for various scenarios were classified based on the established theory of oil-water two-phase flow in horizontal wells; then, detailed flow distribution diagrams were drawn. These data and diagrams presented offer a visual representation of the behavioral patterns exhibited by oil-water two-phase flow under varying situations and form the basis for subsequent model training and testing. Subsequently, based on the experimental data, this study combined the Seagull Optimization Algorithm (SOA) with a BP neural network to effectively learn and predict the experimental data. The SOA optimized the weights and biases of the BP neural network, improving the model’s convergence speed and prediction accuracy. Through rigorous training and testing, an oil-water two-phase flow pattern forecasting model was established, effectively predicting flow patterns under different well deviation, total flow, and water cut conditions. Finally, to validate the efficiency of the established model, a total of 15 data points were chosen from a sample well for validation. By comparing the flow patterns predicted by the model with actual logging data, the results indicate that the model’s accuracy in identifying flow pattern was 86.67%. This demonstrates that the flow pattern prediction model based on the SOA-BP neural network achieved a high level of accuracy under different complicated working conditions. This model effectively fulfills the requirements for dynamic monitoring in actual production. This indicates that the SOA-BP neural network-based flow pattern forecasting method is highly valuable due to its practical application value and provides an efficient technical approach for the development of unconventional reservoirs and the dynamic monitoring of horizontal wells in the future.

Whale Optimization Algorithm BP Neural Network with Chaotic Mapping Improving for SOC Estimation of LMFP Battery

The state of charge (SOC) is a core parameter in the battery management system for LMFP batteries. Accurate SOC estimation is crucial for ensuring the safety and reliability of energy storage applications and new energy vehicles. In order to achieve better SOC estimation accuracy, this article proposes an adaptive whale optimization algorithm (WOA) with chaotic mapping to improve the BP neural network (BPNN) model. The SOC estimation accuracy of the BPNN model was improved by utilizing WOA to find the optimal target weight values and thresholds. Comparative simulation experiments (including constant current and working condition discharge experiments) were conducted in Matlab/Simulink R2018a to verify the proposed algorithm and the other four algorithms. The experimental results show that the proposed algorithm had higher SOC estimation accuracy than the other four algorithms, and its prediction errors were less than 1%. This indicates that the proposed SOC estimation method has better prediction accuracy and stability, and has certain theoretical research significance.

Analysis of Bubble Flow in an Inclined Tube and Modeling of Flow Prediction

The lubricating oil system is a significant component of aviation engine lubrication and cooling, and the scavenge pipe is an essential component of the lubricating oil system. Accurately identifying and understanding the flow state of the scavenge pipe is very important. This article establishes a visualization test bench for a 45-degree inclined scavenge pipe, with upward and downward flow directions, respectively. The test temperature is 370 K, and a high-speed camera captures the changes in the two-phase flow inside the pipeline. Based on high-speed photography photos, we develop software for analyzing the flow characteristics of bubbles inside the tube and explore the influence of gas phase conversion velocity and liquid phase conversion velocity on the apparent velocity of bubbles inside the tube. Multiple algorithms were used to develop the model by combining machine learning with speed and accuracy to establish a data regression prediction model for the apparent velocity of bubbles inside the tube. Through calculation and analysis, it was found that the root mean square error of the prediction model using the BP neural network algorithm was the lowest, and the decision coefficient of the prediction model using the support vector machine algorithm was the highest.

Research on the HHO improved BP neural network fault prediction algorithm for distribution networks based on weather factors

Abstract Weather factors have a substantial impact on the operation and maintenance of the distribution network, but the current system fault prediction platform mainly focuses on the operating characteristics of the system, that is, the internal parameters, and rarely considers the external parameters. Firstly, by studying the fault data of the distribution network in a city in northern China caused by weather factors, this paper obtains the main weather factors affecting the reliability of power supply in this area. Then, the Harris Hawks optimization (HHO) improved BP neural network algorithm is used to establish the main reliability evaluation models suitable for this region, and based on this, the fault probability of this region is predicted under the target weather. Finally, the prediction results are compared with the actual fault data caused by weather factors in this area. The results show that both the PSO-BP algorithm and HHO-BP neural network algorithm achieve better prediction accuracy than the BP neural network algorithm, and the HHO-BP algorithm has better convergence speed.

A Quantitative Seismic Topographic Effect Prediction Method Based upon BP Neural Network Algorithm and FEM Simulation

A Quantitative Seismic Topographic Effect Prediction Method Based upon BP Neural Network Algorithm and FEM Simulation

Multi-objective optimization design for the carcass of marine flexible pipes based on BP neural network and genetic algorithm

Multi-objective optimization design for the carcass of marine flexible pipes based on BP neural network and genetic algorithm

Optimization of magnetic circuit in electro-controlled permanent magnet blank holder process with magnetorheological elastomers and analysis of its impact on deep drawing

Addressing the issue of blank holder force (BHF) attenuation caused by the blank-holder gap in the EPMBH process, this study proposes a solution: employing magnetorheological elastomer (MRE) as a magnetic medium to fill these gaps, thus constructing an optimized edging magnetic circuit aimed at reducing magnetic loss and enhancing BHF. Initially, the study utilizes finite element analysis to build a dataset, followed by the application of a particle swarm to optimization BP neural network for magnetic force prediction and modeling. This process reveals the coupling relationship between the working gap thickness, MRE's relative permeability, and magnetic force, where the training and testing results of the model show an average error is around 5 %. Further, by applying a genetic algorithm (GA) to optimize the magnetic permeability of MRE, the theoretical optimal relative permeability curve of MRE is determined. To facilitate production, the goal is set to achieve 98 % of the maximum magnetic force, based on which further optimization is conducted to establish the engineering design range for MRE's relative permeability. Subsequently, MRE samples were designed and manufactured, and combined with a 36-pole EPM magnetic loading mechanism for experimental validation. The results show that the error between the predicted and experimental outcomes is 5.2 %, and the deep drawing experiments further confirm that the introduction of MRE helps the electronically controlled permanent magnet edging process to accommodate the deep drawing needs of slabs with varying thicknesses. This discovery provides valuable guidance for the improvement of the EPMBH process.

Analysis of Factors Related to Vasovagal Response in Apheresis Blood Donors and the Establishment of Prediction Model Based on BP Neural Network Algorithm

Analysis of Factors Related to Vasovagal Response in Apheresis Blood Donors and the Establishment of Prediction Model Based on BP Neural Network Algorithm

Identification of key biomarkers for early warning of diabetic retinopathy using BP neural network algorithm and hierarchical clustering analysis

Diabetic retinopathy is one of the most common microangiopathy in diabetes, essentially caused by abnormal blood glucose metabolism resulting from insufficient insulin secretion or reduced insulin activity. Epidemiological survey results show that about one third of diabetes patients have signs of diabetic retinopathy, and another third may suffer from serious retinopathy that threatens vision. However, the pathogenesis of diabetic retinopathy is still unclear, and there is no systematic method to detect the onset of the disease and effectively predict its occurrence. In this study, we used medical detection data from diabetic retinopathy patients to determine key biomarkers that induce disease onset through back propagation neural network algorithm and hierarchical clustering analysis, ultimately obtaining early warning signals of the disease. The key markers that induce diabetic retinopathy have been detected, which can also be used to explore the induction mechanism of disease occurrence and deliver strong warning signal before disease occurrence. We found that multiple clinical indicators that form key markers, such as glycated hemoglobin, serum uric acid, alanine aminotransferase are closely related to the occurrence of the disease. They respectively induced disease from the aspects of the individual lipid metabolism, cell oxidation reduction, bone metabolism and bone resorption and cell function of blood coagulation. The key markers that induce diabetic retinopathy complications do not act independently, but form a complete module to coordinate and work together before the onset of the disease, and transmit a strong warning signal. The key markers detected by this algorithm are more sensitive and effective in the early warning of disease. Hence, a new method related to key markers is proposed for the study of diabetic microvascular lesions. In clinical prediction and diagnosis, doctors can use key markers to give early warning of individual diseases and make early intervention.

A prediction method for salt frost resistance of carbon-glass mixed fiber concrete based on improved BP neural network

Abstract Low temperature and freeze-thaw can greatly reduce the service life of concrete, leading to serious safety hazards. Salt frost resistance is one of the important characteristics of concrete. Therefore, in order to more accurately evaluate the salt frost resistance of carbon glass hybrid fiber reinforced concrete. This article proposes a prediction method using measured data based on an improved BP neural network. Firstly, conduct freeze-thaw cycle tests on fiber reinforced concrete with different proportions. Studied the variation law of concrete quality loss rate and dynamic elastic modulus. Then, based on BP neural network and particle swarm optimization algorithm, a salt frost damage prediction model for carbon glass hybrid fiber reinforced concrete was established. Finally, conduct optimization based on the learning samples and compare and analyze the prediction errors of the model. The simulation results demonstrate that the proposed method has good accuracy and stability.

Application of the BP neural network algorithm to the ultimate bearing capacity test of building structures

The ultimate bearing capacity test effect directly influences the safety performance of the design of building structures. To enhance the safety of building structures, applying the BP neural network algorithm to their ultimate bearing capacity test is studied to improve the test effect. The shear wave velocity of the building structure during stress is collected using the static probing technique. The input samples of the BP neural network are the building structure’s shear wave velocity and construction parameters. They are processed by dimensionality reduction through principal component analysis. The firework algorithm is used to optimize the weight of the BP neural network. An early termination training method is designed, and the optimal weight is combined to train the BP neural network. After training, the samples are input after dimensionality reduction, and the building structure’s ultimate bearing capacity test results are output. Experimental results show that this method can effectively collect the shear wave velocity of building structures and complete the dimensionality reduction of samples. Under different coaxial stresses, this method can effectively measure the ultimate bearing capacity, about 3800 kN. After parameter optimization, the test value of this method is very close to the target value; that is, the ultimate bearing capacity test precision is high.

Optimization of five-axis tool grinder structure based on BP neural network and genetic algorithm

Optimization of five-axis tool grinder structure based on BP neural network and genetic algorithm

Design of partial discharge detection device based on BP neural network algorithm

Abstract At present, with the rapid development of science and technology, the construction of new power systems has been promoted rapidly, and the automation and intelligence of power network operation equipment has been continuously improved. It is increasingly important to use various detection techniques for the state detection of power grid operating equipment. In this paper, a design scheme of partial discharge detection device based on neural network BP algorithm is achieved, combining with the current detection technology and embedded technology. The purpose of discovering and evaluating the insulation defects of power grid equipment is to detect partial discharge.

Optimized Design for Armature Lightweighting and Contact Pressure Distribution Uniformization Based on BP Neural Network and Genetic Algorithm

Optimized Design for Armature Lightweighting and Contact Pressure Distribution Uniformization Based on BP Neural Network and Genetic Algorithm

Application of NGO-BP Neural Network in Battery Life Prediction of Portable Medical Devices

The development of portable medical devices cannot be separated from safe and efficient batteries. Accurately predicting the remaining life of batteries can greatly improve the reliability of batteries, which is of great significance for portable medical devices. This article focuses on the high dependence of the BP neural network algorithm on initial weights and thresholds, as well as its tendency to fall into local minima. The Northern Goshawk Optimization (NGO) algorithm is used to optimize the BP neural network and to test the 18650 lithium battery data under different ambient temperatures (4, 24, 43°C) typical of medical equipment. The experimental results show that the NGO algorithm can significantly improve the prediction accuracy of the BP neural network under various temperature conditions, achieving accurate and effective prediction of the remaining battery life.