BP Neural Network Algorithm Research Articles

Prediction Model of Word EERIE Based on BP Neural Network

This paper uses BP neural network algorithm to determine the difficulty level of the word EERIE by constructing a neural network model, and determine its relationship with the word length, word commonness, letter frequency and other attributes to predict the relevant percentage of the future date, and discusses the uncertainty of the model, the accuracy of the classification model and other characteristics of the data set. In terms of accuracy, the random forest algorithm is the best, 72%, and the k-nearest neighbor algorithm is the worst. In terms of F1 score, logistic regression algorithm performs the best conclusion.

A New Method for Erosion Prediction of 90° Elbow Based on Non-Axisymmetric Ultrasonic-Guided Wave and the PSO-LSSVM Algorithm.

The non-axisymmetric exciting guided wave can detect the thinning section of the elbow, and the time domain energy value of the signal collected at the outer arch position of the receiving end displays a downward trend as the remaining thickness of the erosion area decreases. To address the difficulty in detecting the erosion degree of the elbow with high accuracy, this paper uses the linear frequency modulation (LFM) signal to excite a non-axisymmetric guided wave that propagates in the 90° elbow and collects signals through four PZT receivers. To predict the erosion degree, the corresponding relationship between the energy value of the four signals after fractional Fourier filtering and the degree of elbow erosion is established through the particle swarm optimization (PSO)-least squares support vector machine (LSSVM) algorithm. The results show that the method proposed has an average accuracy rate of 98.1864%, 94.7167%, 99.119%, and 99.9593% for predicting the erosion degree of four elbow samples, and 94.0039%. and 81.2976% for two new erosion degrees, which are higher than the nonlinear regression model, LSSVM algorithm, and BP neural network algorithm. This study has guiding significance for real-time monitoring of elbow erosion.

Application and Comparison of Machine Learning Methods for Mud Shale Petrographic Identification

Machine learning is the main technical means for lithofacies logging identification. As the main target of shale oil spatial distribution prediction, mud shale petrography is subjected to the constraints of stratigraphic inhomogeneity and logging information redundancy. Therefore, choosing the most applicable machine learning method for different geological characteristics and data situations is one of the key aspects of high-precision lithofacies identification. However, only a few studies have been conducted on the applicability of machine learning methods for mud shale petrography. This paper aims to identify lithofacies using commonly used machine learning methods. The study employs five supervised learning algorithms, namely Random Forest Algorithm (RF), BP Neural Network Algorithm (BPANN), Gradient Boosting Decision Tree Method (GBDT), Nearest Neighbor Method (KNN), and Vector Machine Method (SVM), as well as four unsupervised learning algorithms, namely K-means, DBSCAN, SOM, and MRGC. The results are evaluated using the confusion matrix, which provides the accuracy of each algorithm. The GBDT algorithm has better accuracy in supervised learning, while the K-means and DBSCAN algorithms have higher accuracy in unsupervised learning. Based on the comparison of different algorithms, it can be concluded that shale lithofacies identification poses challenges due to limited sample data and high overlapping degree of type distribution areas. Therefore, selecting the appropriate algorithm is crucial. Although supervised machine learning algorithms are generally accurate, they are limited by the data volume of lithofacies samples. Future research should focus on how to make the most of limited samples for supervised learning and combine unsupervised learning algorithms to explore lithofacies types of non-coring wells.

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.

Retracted: Application of Art Activities in Colleges and Universities Based on BP Neural Network Algorithm

Retracted: Application of Art Activities in Colleges and Universities Based on BP Neural Network Algorithm

Mid-infrared carbon isotope spectrum logging system combined with a thermostat of optical subsystem for high-precision detection of isotope ratio

In the process of oil and gas exploration and development, carbon isotope ratio can reflect the maturity of oil and gas and predict the recovery factor, and the isotope ratio in the composition of shale gas is particularly important. Thus, a carbon isotope spectrum logging system was designed and exploited based on tunable diode laser absorption spectroscopy (TDLAS) technology under the fundamental frequency absorption band of 12CO2 and 13CO2 molecules, and a quantum cascade laser (QCL) with center wavelength of 4.35 μm was applied. For further detection sensitivity, wavelength modulation spectroscopy (WMS) technology was combined to suppress background noise through the modulation of QCL. A multi-pass gas cell (MPGC) with an optical path length of 41 m was utilized for lower limit of detection (LoD). In order to suppress the temperature dependence of the absorption spectrum, the optical subsystem was placed in a high-precision thermostat to maintain a stable temperature, so as to achieve high-precision and high-stability detection. Meanwhile, sparrow search algorithm-back propagation (SSA-BP) was applied for concentration prediction of 12CO2 and 13CO2. Taking advantage of the excellent optimization ability, fast convergence speed and high stability of SSA, the problem that BP neural network algorithm is highly dependent on initial value can be solved to some extent. Sensor performance was validated through calibration and stability experiments. The LoD of 12CO2 reached a minimum of 0.618 parts-per-billion (ppb) with an 88 s averaging time, and the LoD of 13CO2 reached 0.181 ppb when the averaging time was 96 s. Besides, the standard deviation of carbon isotope ratio obtained by this system was ∼ 0.61 ‰. The results illustrate that this self-developed sensor has a bright prospect in the field of shale gas isotope detection.

Optimized Design of Large-Body Structure of Pile Driver Based on Particle Swarm Optimization Improved BP Neural Network

Optimization of the pile driver’s large-body structure is important to achieve the driver’s overall light weight. This paper studies the large-body structure of a hydraulic static pile driver. We used the APDL parametric design language provided by ANSYS to construct a geometric model of the large-body structure and performed a static analysis of the finite element model. Under the assumption that the strength and stiffness meet the design requirements, the optimization model was constructed with the thickness of each plate of the large-body structure as the design variable, the structural strength and stiffness as the constraints, and the minimum mass as the objective function. Finally, two optimization algorithms were used to solve the model, and the comparison of the two sets of solutions shows that the improved BP neural network algorithm based on the particle swarm optimization algorithm performs better. The optimized mass of the large-body structure was reduced from 82,556.1 kg to 65,046.15 kg, a mass reduction of 21%. The lightweight design of the pile driver was achieved.

Application of interactive information system in college personnel management by using BP neural network algorithm

Application of interactive information system in college personnel management by using BP neural network algorithm

The MFL detection method by the gradient hall sensors array for the cracks on the top surface of the rails

Cracks on the top surface of the rails due to contact fatigue are usually narrow and variable in direction. Thus, more spatial features need to be explored when using the magnetic flux leakage detection method. In this article, we proposed a way to predict the size and direction of the cracks by the gradient hall sensors array. First, the experimental equipment was designed for leakage field signal detection. Then, the peak time differences of the leakage field signal were analyzed to predict the crack angle, and the magnetic gradient modulus was calculated to predict the crack width. Furthermore, the principal component analysis was performed to reduce the number of eigenvalues. With that, BP neural network algorithm was used to predict the length and depth of the cracks. The experimental results indicate that the method can improve the accuracy of crack prediction, and the average relative errors of the prediction for crack angle, width, length, and depth were 5.7%, 15.3%, 9.2%, and 11.2%, respectively.

Parameter identification of contact model for tank saddle ring system based on genetic algorithm and BP neural network

This paper is devoted to present an experimental identification method of tank saddle ring system parameters based on BP neural network algorithm and genetic algorithm. The contact parameters in mechanical system cannot be obtained directly through measurement, and it is inevitable that there will be errors between the parameters estimated by engineering experience and the real value. In this paper, a 6-degree-of-freedom swing experimental device of tank turret is built, the excitation source is 1Hz sine function, the contact stiffness and contact damping coefficient are identified by constructing error functions based on simulation results and data of four laser displacement sensors distributed around the saddle ring, by the combination of BP neural network and genetic algorithm. Then, the sensor data in the test results and simulation results are compared to further verify the accuracy of the turret model and parameter identification method.

Design of a Live Pig Breeding System Based on ZigBee

This paper designed a breeding system based on ZigBee. The system consists of an environment detection module based on ZigBee technology and a video monitoring module based on BP neural network algorithm. The environment detection module is a wireless sensor network based on ZigBee technology. The sensors with different functions interact with the data through the ZigBee wireless communication module, and monitor the environment under the coordinated control of the router and the main control. The video surveillance module adopts the deep learning mode based on BP neural network algorithm, with Raspberry PI as the main control unit, and equipped with deep learning algorithm to identify the body temperature and behavior of pigs, and facilitate the management of pigs by users. The system can effectively monitor the environment of pig houses, the behavior and health of pigs, and improve the efficiency of breed ink the data.

Performance Study of a Permanent-Magnet Eddy Current Damper for Guns Recoil Control

Eddy current damper (ECD) is a promising device for dissipating kinetic energy in dynamic systems, this study is conducted to analyze the dynamic characterization and structural optimization of linear ECD under guns launch load. The recoil resistance of ECD under guns launch load is highly nonlinear at high recoil velocity, and the recoil resistance curve shows an increase-decrease-increasing shape like a “saddle”, which is not conducive to the stability of ECD during operation. The effects of several structural parameters on the dynamic characterization of ECD were studied in details. Results show that the air-gap size, the thickness of the magnetic conduction tube and conductive tube all have a notable influence on the recoil resistance curve of ECD. Moreover, BP neural network and genetic algorithm were used for structural optimization and the optimal value was verified by FEM. The optimization results indicate that the “saddle” shape of optimized recoil resistance curve is basically invisible. The fullness of the recoil resistance curve is significantly improved from 84.93% to 92.1%, which guaranteeing a smooth recoil movement. Finally, an impact loading test system for eddy current damper was built and the experimental results were basically consistent with the simulation results.

The Influence of a CGA-BP Neural-Network-Based Aeration Oxygen Supply Prediction Model on the Maturity of Aerobic Composting

In order to improve the problem of low oxygen supply efficiency during aerobic composting and prolong composting maturity, a genetic algorithm was used to optimize the initial weights and thresholds of the standard BP neural network and obtain the optimal parameters, and then a clonal selection algorithm was used to optimize the mutation operator in the genetic algorithm and duplicate the operator. A CGA-BP neural network based on an aeration oxygen supply prediction model was constructed, and the aeration oxygen supply predicted by the model was used to ferment the compost and accelerate the process of compost maturation. The results show that compared with the standard BP neural network algorithm and the GA-BP neural network algorithm, this model has accurate prediction performance in predicting aeration oxygen supply, with a prediction accuracy of 99.26%. The aeration oxygen supply predicted based on the CGA-BP model can effectively promote the composting maturity process and meet the needs of aeration oxygen supply throughout the entire fermentation process of aerobic compost.

Early Warning of Poverty Returning against the Background of Rural Revitalization: A Case Study of Two Counties in Guangxi Province, China

China has achieved the goal of building a moderately prosperous society in a well-rounded way by 2020. At this stage, effectively dealing with poverty and not returning to it has become the bottom-line task of rural revitalization. The purpose of this study is to construct a poverty-return early warning and evaluation system for X and Y counties in Guangxi. Based on the field survey data of 150 households from the questionnaire survey in X County and Y County of Guangxi Province, an early warning evaluation system for returning to poverty in the two counties of Guangxi Province is constructed. The AHP analytic hierarchy process is used to evaluate the early warning of returning to poverty for farmers. The BP neural network algorithm is used to verify the rationality of the method; the overall poverty relief situation in the two counties is stable and the living conditions are good. The early warning results are as follows: One household in X County has a severe early warning, six households have a slight early warning, and sixty-four households have no early warning; in Y County, six households had severe early warning, six households had mild early warning, and sixty-seven households had no early warning. For farmers, serious early warnings are mainly caused by the lack of labor force and low annual per capita net income, as well as the lack of the main means of livelihood and capacity. The characteristics of mild early warnings for farmers are mainly that the proportion of non-labor income is relatively high, and the farmers lack the ability and way of long-term development. Different suggestions are put forward for farmers with different early-warning levels, focusing on improving their viability and development ability.

Application of toroidal magnet excitation and hall sensor array in magnetic leakage field detection of rail surface defect

To detect rail top surface defects, NdFeB toroidal permanent magnets were firstly used to excite the rail detection area, and the magnetic excitation direction was perpendicular to the rail direction. So that, the magnetic circuit can be analyzed, and the model was verified by finite element simulation. Secondly, the artificial rectangular defect samples were machined on the top surface of the rail, and the magnetic field data acquisition system was developed and equipped on the inspection vehicle. With that, a linear array which consisted of three-dimensional hall sensors was designed to detect the magnetic leakage field signal from the defect samples. Finally, the features of the leakage field signal in each dimension were extracted. And then, the length, width, and depth of the defects were predicted using BP neural network algorithm. The results indicate that the method can improve the excitation efficiency, and the average absolute errors of prediction for defect length, width, and depth were 1.43 mm, 1.01 mm, and 0.63 mm respectively.

RETRACTED ARTICLE: Big data simulation of science and technology finance supporting Shaanxi urban–rural integration development based on BP neural network algorithm

In the context of the current era, if China wants to implement the strategic goal of rural revitalization, it must promote the integrated development of urban and rural areas. This paper takes Shaanxi Province as the main research goal, which is at the strategic intersection of the "the Belt and Road", has an important position, can affect the process of regional coordination and urban and rural integration throughout the country, and is a major province of characteristic agriculture and a national transportation hub. In this context, this paper uses BP neural network algorithm to simulate and analyze the coordinated development of urban and rural areas based on the actual development of science and technology finance in Shaanxi Province. After obtaining the economic status data of the province, this paper builds a regional characteristic platform that can provide targeted science and technology finance services, which can achieve diversified processing, complete multi entity fund supply and maintain the transparency of information between the supply and demand sides. Based on the field survey and literature review and analysis, it can be seen that the ten prefecture level cities included in the study area and province have different levels of coordinated development of urban and rural economy, and there is a Matthew effect characteristic of uneven strength, so the regional differences show a weakening feature, and they need to be adjusted gradually to make them develop in an orderly manner. Based on the research conclusion, this paper carries out the construction of urban–rural economic coordinated development and scientific and technological financial model support, and analyzes the financial support function and actual effect of the province in detail. In this paper, the BP neural network algorithm is applied to the integration of urban and rural development in Shaanxi Province to conduct big data simulation for science and technology finance.

Research on Propellant Performance Prediction Based on Neural Network or Algorithm Optimization

In order to analyze the four main properties of propellant, the performance prediction model based on BP neural network is established with LM algorithm and CG algorithm as the core algorithm, and the simulation calculation is carried out with the actual production process data and the performance of the model is analyzed. The results show that the mass prediction model based on BP neural network algorithm can quickly predict the properties of propellant. Compared with the prediction model based on CG algorithm, the prediction model based on LM algorithm is more suitable for propellant performance prediction and analysis.

Construction Schedule of Medium Voltage Overhead Distribution Network Optimization Based on Neural Network Algorithm

INTRODUCTION: The medium voltage overhead distribution network is a complex planning problem, and monitoring the construction progress of the medium voltage overhead distribution network is an important aspect of the planning problem. Regarding the structure of the medium voltage overhead distribution network, it is one of the important components in China's distribution network system. If the construction progress of the medium voltage overhead distribution network is optimized, it will affect the normal operation of the entire power grid system in a certain region. Optimizing the construction progress of medium voltage overhead distribution networks using modern development related technologies or algorithms is one of the practical research topics. OBJECTIVES: To better predict various issues that may arise during the construction progress of the distribution network. Complex geographical conditions lead to construction difficulties, and lack of technology leads to frequent stoppages of construction, further leading to frequent power outages in various regions. In terms of the overall power supply system, the reliability of power supply is not high. METHODS: By comparing the BP neural network algorithm with the CNN network algorithm, the actual operation effect is evaluated by experts. RESULTS: This article predicts the implementation progress of the medium voltage overhead distribution network, and the results show that the accuracy of the BP model for predicting the construction progress of the medium voltage overhead distribution network can reach 88%; The accuracy of the CNN medium voltage overhead distribution network construction progress prediction model reaches 77%. CONCLUSION: The use of neural network algorithms to optimize the construction schedule of medium voltage overhead distribution networks has been evaluated by experts in practical applications, and the medium voltage overhead distribution network in power supply systems has potential application value.

Safety Risk Evaluation of Tourism Management System Based on PSO-BP Neural Network

With the recovery of the tourism industry and the development of the field of artificial intelligence, the application of intelligent neural network technology to management systems for safety risk assessment is the choice of the times and an urgent real need for relevant practitioners. In this study, the BP neural network algorithm is used as a tool for safety evaluation of the tourism management system. The three-layer structure of the BP neural network and the role of nodes in it are introduced, the weight values and thresholds of nodes in each of the three layers are calculated, and the particle swarm algorithm is added to optimize the model. In the practical stage, the tourism data of a place in Switzerland was selected as the training data. After 12,859 iterations, the model achieved the best calibration error of 0.126. After 300 iterations of learning, the BP algorithm optimized with the PSO algorithm has a faster convergence rate, which indicates that the performance of the optimized algorithm has improved significantly and has the global search capability that was not available before, which significantly outperforms the FastText and LSTM models. With the increase in the number of samples of tourism data, macroaccuracy always remains above 80%, so it proves that the optimization algorithm used in this study is an effective and reliable model.

Prediction and Control of Thermal Transport at Defective State Gr/h-BN Heterojunction Interfaces.

The control of interfacial thermal conductivity is the key to two-dimensional heterojunction in semiconductor devices. In this paper, by using non-equilibrium molecular dynamics (NEMD) simulations, we analyze the regulation of interfacial thermal energy transport in graphene (Gr)/hexagonal boron nitride (h-BN) heterojunctions and reveal the variation mechanism of interfacial thermal energy transport. The calculated results show that 2.16% atomic doping can effectively improve interfacial heat transport by more than 15.6%, which is attributed to the enhanced phonon coupling in the mid-frequency region (15-25 THz). The single vacancy in both N and B atoms can significantly reduce the interfacial thermal conductivity (ITC), and the ITC decreases linearly with the increase in vacancy defect concentration, mainly due to the single vacancy defects leading to an increased phonon participation rate (PPR) below 0.4 in the low-frequency region (0-13 THz), which shows the phonon the localization feature, which hinders the interfacial heat transport. Finally, a BP neural network algorithm is constructed using machine learning to achieve fast prediction of the ITC of Gr/h-BN two-dimensional heterogeneous structures, and the results show that the prediction error of the model is less than 2%, and the method will provide guidance and reference for the design and optimization of the ITC of more complex defect-state heterogeneous structures.