BP Neural Network Algorithm Research Articles

Evaluation of Electric Vehicle Integrated Charging Safety State Based on Fuzzy Neural Network

Electric vehicles have been promoted worldwide because of their high energy efficiency and low pollution. However, frequent charging safety accidents have to a certain extent restricted the development of electric vehicles. Therefore, it is extremely important to accurately evaluate the safety state of EV charging. The paper presents an integrated safety assessment method for electric vehicle charging safety based on fuzzy neural network. The integrated fault model was established by analyzing the correlation between truck–pile–grid. Then the integrated evaluation index was analyzed and sorted out, and the comprehensive fuzzy evaluation method used to evaluate. Following this, the improved GA_BP neural network algorithm was used to calculate the weight. Compared with the evaluation effect before and after the improvement, the simulation results show that the GA_BP neural network has higher accuracy and smaller error than the ordinary BP neural network. Finally, the feasibility and effectiveness of the evaluation method was verified by a case study.

An Adaptive Dance Motion Smart Detection Method Using BP Neural Network Model under Dance Health Teaching Scene.

As a body movement art, dance has its special form of expression. In terms of dance vocabulary, it can be roughly divided into two parts: external body movement and internal modality. In the process of body movement, it conveys information through silent language and the audience directly feels the information given by the dance image through vision. This is the special way of expressing emotion and meaning in dance art. This paper combines artificial intelligence technology and BP neural network (BPNN) algorithm to intelligently control dance teaching and solve complex nonlinear control problems. This paper studies dance teaching based on artificial intelligence technology. In this paper, BPNN algorithm and PCA-BPNN algorithm are used to test the dance teaching training of dance language, dance music, and stage art. The average accuracy of the BPNN evaluation model is 85.35% when the time reaches 80, while the average accuracy of the PCA-BPNN evaluation model is 65.64%. This shows that the accuracy of the BPNN evaluation model is higher than that of the PCA-BPNN evaluation model. Under the artificial intelligence technology, the dance using BPNN algorithm brings more intense sensory stimulation to the viewer because of the accompaniment of music, so as to achieve the infection and enjoyment of beauty and achieve the harmonious unity of sports and art.

Research and application of ethanol coupling to C4 olefins based on BP neural network algorithm

In order to explore the influence of catalyst combination on the preparation of C4 olefins by ethanol catalytic coupling, this paper first studied each catalyst by polynomial regression method to analyze the relationship between ethanol conversion, C4 olefin selectivity and temperature. Secondly, BP neural network algorithm is used to select the best catalyst combination to obtain the highest C4 olefin yield. The results show that: 1. Most ethanol conversion and C4 olefin selectivity are directly proportional to temperature. 2. Without considering the temperature, when Co/SiO2 is 3%, the temperature is 400 ℃, Co / SiO2 and HAP are 200mg, The C4 yield is the best (47.23%). When the set temperature is lower than 350 ℃ and the temperature is 325 ℃, Co/SiO2 and HAP are 266mg and 133mg respectively, the maximum yield is 18.10%.

Retracted] Discrete Dynamic Modeling Analysis of Rural Revitalization and Ecotourism Sustainable Prediction Based on Big Data

With the development of modern rural tourism, it has become people’s spiritual demand to deeply tap the characteristic rural culture and continuously enhance the attraction of rural tourism. However, due to the complex tourism market environment, small changes in factors will affect the final result. Therefore, traditional methods cannot accurately predict the future tourism market. In view of this situation, this paper uses the mathematical algorithm of BP neural network to simulate the structure of brain neural network to deal with this kind of nonlinear relationship problem. Through the classification of complex and changeable factors, the modeling is completed systematically, and finally the effective prediction results are obtained. For the analysis of the time evolution of tourism resources development, it can be found from the policy development theory that the innovative development presents the characteristics of S‐shaped curve in time; that is, the number adopted in the initial stage of development is less, then enters the rapid development period, and finally develops slowly. This study will help to solve the primary problem of balancing the positive and negative effects of rural tourism development and realizing the sustainable development of coastal villages.

Analysis of Teaching Effect of Korean Education Course Based on Data Acquisition Technology.

Teaching evaluation is a comprehensive judgment of teachers' teaching effect and students' learning outcomes. It is an essential basis for comprehensive curriculum reform. There are many teaching evaluation systems for Korean majors, generally based on teachers' behavior discrimination and ignoring students' learning process and effect. The existing teaching evaluation system has problems such as heavy workload, slow calculation speed, and intense subjectivity. Based on the characteristics of Korean courses, this study constructs a teaching rating system for Korean courses in universities centered on language learning through data collection, correlation analysis, association rules, and other methods to optimize the student teaching evaluation index. At the same time, the machine learning algorithm is introduced into the teaching evaluation process to construct the teaching evaluation model and realize the automation of the teaching evaluation process. The weighted Bayesian incremental learning method is used to solve the cumulative problem of data acquisition samples. The experimental results show that the accuracy rate of classification using the weighted naive Bayesian algorithm to construct the model can reach 75%. Obviously, due to the traditional Bayesian algorithm and BP neural network algorithm, it is suitable for the teaching evaluation model of Korean majors. It provides a theoretical basis for the development of language education informatization.

Analysis of Human Resource Allocation Model for Tourism Industry Based on Improved BP Neural Network

This paper analyzes the deficiencies of human resource allocation in the tourism industry by investigating the human resource allocation in the tourism industry, puts forward corresponding improvement measures and suggestions, and strives to provide certain guidance and helpful effects for the construction of tourism resource informatization. In this paper, a modified BP neural network model is proposed by introducing random perturbation terms on the hidden layer in the BP neural network algorithm, and the weight matrix connecting the input values is added with the random perturbation matrix to obtain a new weight matrix so that the convergence effect of the improved BP neural network algorithm is improved. Then, to address the problem that the initial weights of the long and short‐term memory neural network and gated BP unit neural network have a large impact on the convergence speed and prediction accuracy of the algorithm after the initial weight selection is determined, this paper introduces the random perturbation term into the gate structure of the long and short‐term memory neural network and gated BP unit neural network and proposes and connects an improved long and short‐term memory neural network and gated BP unit neural network. The weight matrix of the input values is added with the random perturbation matrix to obtain the new weight matrix so that the convergence effect of the improved long and short‐term memory neural network algorithm and the gated BP unit neural network algorithm is improved. Constructing the human resource allocation model of the tourism industry and proposing coping strategies and countermeasures and taking the human resource allocation system of the tourism industry as the core, the human resource allocation model of the tourism industry is established by combining the network image crisis life cycle system of tourism scenic spots and the network public opinion dissemination model. From the perspective of managers, the human resource allocation management policy and management procedures of the tourism industry are proposed. Using the quantifiable and disenable characteristics of online text information, the response strategy of online monitoring and propaganda and offline management and enhancement is proposed, and innovative countermeasures to the human resource allocation of the tourism industry are proposed in three categories: network originated, reality coexisting, and reality originated. Through this paper, we propose a new approach to human resource allocation management and development in the tourism industry and improve the efficiency of human resource allocation in the tourism industry.

A High-Accuracy and Fast Retrieval Method of Atmospheric Parameters Based on Genetic-BP

In this article, we propose an accurate and fast method that is based on the genetic algorithm and the Back-Propagation neural network (GABP) to improve the accuracy and speed of retrieving atmospheric parameters (temperature, humidity, liquid water content, and so on). Firstly, the genetic algorithm is applied to improve and optimize the connection weights and thresholds of the BP neural network according to situ data measured by an sixteen-channel ground-based radiometer and a radiosonde, and then obtain a more proper range of those weights and thresholds. Secondly, the BP neural network is trained once again to get an ideal BP neural network model in a relatively short time. Finally, the model (or GABP), in which a genetic algorithm (GA) is combined with the BP neural network algorithm, is tested by the situ measurements of the sixteen-channel microwave radiometer. The tested results by GABP are not only in a good agreement with those of other known models, and but also has better accuracy and faster convergence speed than those reported algorithms for retrieving atmospheric parameters. It can make a conclusion that GABP is a new algorithm which is capable of quickly retrieving accurately atmospheric parameters.

Evaluation Method of Partial Discharge Evolution Degree of Cable Intermediate Joint

The evolution process of partial discharges generated under typical air gap defects of 10kV XLPE cable intermediate joints was experimentally studied by the built experimental model. In the experimental process, five characteristic parameters representing the severity of partial discharge are extracted, and their change trends with time are analyzed, and the value ranges of various characteristic parameters are divided. Using BP neural network algorithm, the nonlinear relationship network between PD severity and multiple characteristic parameters is established, and the evaluation system of partial discharge severity of cable intermediate joint is constructed. Finally, the correctness and effectiveness of the method is verified by the example testing.

The Prediction Model and System of Stock Rise and Fall Based on BP Neural Network

The purpose of forecasting the rise and fall of the stock market is for investors to analyze the future stock price trends, trends and other information, and combine the characteristics of the stocks to choose appropriate methods to make trading decisions. The purpose of this paper to study the stock price prediction model is to improve the preventive measures against stock risks. This article mainly uses the experimental method and the comparative method to conduct an experimental research on the stock price fluctuation forecast model. Experimental results show that the accuracy of BP neural network algorithm in stock prediction can reach 91.5%. This means that the stock price prediction model based on BP neural network has certain feasibility, and its research value is also relatively large.

Calculation Method of Voltage Stability Based on Neural Network

With the deepening of the research on voltage stability(VS), the methods for analyzing VS have also been further developed. Although the probability of voltage collapse has been greatly reduced compared to before, the research on VS has always been a hot topic due to the seriousness of its consequences. This paper studies the sensitivity method of VS and other related calculation methods, corrects the power system through BP neural network, compares the load margin values obtained by the VS calculation method before and after the correction, and finds that the load margin obtained by the BP neural network algorithm proposed in this paper degree value error is minimal.

Facial feature point recognition method for human motion image using GNN

<abstract> <p>To address the problems of facial feature point recognition clarity and recognition efficiency in different human motion conditions, a facial feature point recognition method using Genetic Neural Network (GNN) algorithm was proposed. As the technical platform, weoll be using the Hikey960 development board. The optimized BP neural network algorithm is used to collect and classify human motion facial images, and the genetic algorithm is introduced into neural network algorithm to train human motion facial images. Combined with the improved GNN algorithm, the facial feature points are detected by the dynamic transplantation of facial feature points, and the detected facial feature points are transferred to the face alignment algorithm to realize facial feature point recognition. The results show that the efficiency and accuracy of facial feature point recognition in different human motion images are higher than 85% and the performance of anti-noise is good, the average recall rate is about 90% and the time-consuming is short. It shows that the proposed method has a certain reference value in the field of human motion image recognition.</p> </abstract>

Application Research of BP Neural Network Algorithm in Power Line Loss Calculation

In recent years, it is still a difficult problem to calculate the line loss of distribution system. The main reason is that China’s power system data is not perfect, the components are huge, the management function does not exist, the network structure is directly connected. Therefore, reducing the loss rate of power system has become an urgent problem for energy companies to solve. Developing effective methods for line loss reduction evidence and line loss assessment requires careful calculation and analysis of line loss, which ultimately contributes to and guides line loss management.

Entrepreneurial Mentality Analysis for College Students Based on the Improved BP Neural Network.

In this study, the fuzzy comprehensive evaluation model is used to evaluate the characteristics of college students' entrepreneurial psychology, and a prediction model of college students' entrepreneurial psychology characteristics is established, which is simulated by Matlab to achieve good validity. Based on the research on the characteristics of college students' entrepreneurial psychology, this study proposes a design method of indicators and parameters for evaluating the characteristics of college students' entrepreneurial psychology. In this study, the genetic algorithm is used to optimize the BP neural network. The optimized neural network greatly improves the global search and local search capabilities. The performance of the model is tested through simulation tests. Through the simulation comparison test between the improved model and the standard model, the results show that the model can predict the entrepreneurial psychological characteristics of college students. By comparing the improved BP neural network algorithm with the original algorithm simulation experiment, the improved BP neural network improves the sensitivity by 20%, the specificity by 5%, and the accuracy by 8%.

Application of the BP Neural Network Model of Gray Relational Analysis in Economic Management

According to the BP neural network algorithm, for the problem that the input variables of the BP network cannot be automatically optimized during the modeling process of the multivariable complex system, it is used to establish a neural network optimization algorithm according to the gray correlation analysis (GM2 BPANN). Using the data of China’s grain production forecast, the stepwise regression method and the gray GM (1, N) model method were compared and tested. The results show that the new model can comprehensively and extensively process a large number of input variables by using the concept of the gray correlation degree, without having to go through special subjective screening and hence improving the adaptability of the BP network, and at the same time, it can obtain better prediction accuracy and stability. Through the empirical test, the prediction ability of the three methods of regression, GM (1, N) gray system, and GM‐BPANN model is compared. It is proved that the GM‐BPANN optimization algorithm that combines the gray relational analysis and BP neural network method can enhance the multivariable processing ability and network adaptability of the BP network and has good prediction accuracy and stability.

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.

Research on the rapid design technology of automotive inspection structure based on MBD model

In view of the specificity and low efficiency of the design of automobile inspection fixture, a deformation design method of inspection fixture based on BP neural network algorithm is proposed. BP neural network algorithm is used to realize the learning and classification of case knowledge, and FCM (fuzzy c-means) algorithm and kernel principal component analysis are used to optimize the information source to improve the retrieval efficiency and accuracy. Based on the analysis of the existing fixture design case structure, the case structure is skeletonized to increase the applicability of the case structure. At the same time, the case frame structure is associated with the size chain, the priority deformation rule is proposed, and the relationship of each size chain is established to realize the mutual adjustment of each size chain. From the similarity of retrieval cases, the paper proposes the design scheme of inspection tools to improve the design efficiency. Finally, taking the front bumper model as the experimental object, the deformation rules are compared, and the priority deformation rule is more accurate than the ordinary basic rule. Compared with the manual design, the design efficiency of this method is improved by 55.71%, which proves the feasibility of this method.

Mathematical model of transforming image elements to structured data based on BP neural network

Abstract The analysis and structural transformation of power-related picture elements is an essential result of regional power grid research. This paper proposes a new idea for extracting monolithic insulator images based on analysing the characteristics of scanned colour grid power insulators. At the same time, the article extracts the RGB colour matrix of the insulator based on the BP neural network algorithm. Then, it uses it as a characteristic parameter for training and analysis. Combining the characteristics of image data, it is found that the model proposed in this paper enhances the ability to express images, thereby improving the accuracy of image classification. Furthermore, many experiments on the accurate data set of insulator monoliths show the effectiveness of this model.

Research on Modeling and Dynamic Characteristics of Complex Biological Neural Network Model considering BP Neural Network Method

Biological neural network system is a complex nonlinear dynamic system, and research on its dynamics is an important topic at home and abroad. This paper briefly introduces the dynamic characteristics and influencing factors of the neural network system, including the effects of time delay and noise on neural network synchronization, synchronous transition, and stochastic resonance, and introduces the modeling of the neural network system. There are irregular mixing problems in the complex biological neural network system. The BP neural network algorithm can be used to solve more complex dynamic behaviors and can optimize the global search. In order to ensure that the neural network increases the biological characteristics, this paper adjusts the parameters of the BP neural network to receive EEG signals in different states. It can simulate different frequencies and types of brain waves, and it can also carry out a variety of simulations during the operation of the system. Finally, the experimental analysis shows that the complex biological neural network model proposed in this paper has good dynamic characteristics, and the application of this algorithm to data information processing, data encryption, and many other aspects has a bright prospect.

Identification of key volatile organic compounds in aircraft cabins and associated inhalation health risks

The identification of key VOCs during flights is important in creating a satisfactory aircraft cabin environment. Two VOC databases for the building indoor environment (from 251 occupied residences) and the aircraft cabin environment (from 56 commercial flights) were compared, to determine the common compounds (detection rate (DR) > 70%) in the two environments and the characteristic VOCs (only those with high DR during flights) in aircraft cabins. Possible VOC emission sources in flights were also discussed. As TVOC is usually viewed as a general indicator of air quality, the prediction of TVOC concentration was carried out using BP neural network algorithm, and the average error between the predicted and measured values was 55.35 μg/m3 (R2 = 0.80). Meanwhile, the VOCs’ inhalation cancer/non-cancer risks to crew members and passengers were calculated on the basis of detection rates, exposure concentrations, and health risk assessments. Six compounds (i.e., formaldehyde, benzene, tetrachloroethylene, trichloromethane, 1,2-dichloroethane, and naphthalene) were proposed as the key VOCs in the existing aircraft cabin environment, presenting a risk to crew members that is higher than the US EPA proposed acceptable level (evaluated mean value > 1E-06). The estimated lifetime excess cancer/non-cancer risks for passengers were all below the assessment criteria. Based on a summary of various VOC limits in five built environments, hierarchical design of VOC concentration limits is recommended for the aircraft environment.

Design of and Research into a Multiple-Fuzzy PID Suspension Control System Based on Road Recognition

By analyzing the shortcomings of the traditional fuzzy PID(Abbreviation for Proportional, Integral and Differential) control system (FPID), a multiple fuzzy PID suspension control system based on road recognition (MFRR) is proposed. Compared with the traditional fuzzy PID control system, the multiple fuzzy control system can identify the road grade and take changes in road conditions into account. Based on changes in road conditions and the variable universe and secondary adjustment of the control parameters of the PID controller were carried out, which makes up for the disadvantage of having too many single input parameters in the traditional fuzzy PID control system. A two degree of freedom 1/4 vehicle model was established. Based on the suspension dynamic parameters, a road elevation algorithm was designed. Road grade recognition was carried out based on a BP neural network algorithm. The experimental results showed that the sprung mass acceleration (SMA) of the MFRR was much smaller than that of the passive suspension system (PS) and the FPID on single-bump and sinusoidal roads. The SMA, suspension dynamic deflection (SDD) and tire dynamic load (TDL) of the MFRR were significantly less than those of the other two systems on roads of each grade. Taking grade B road as an example, compared with the PS, the reductions in the SMA, SDD and TDL of the MFRR were 40.01%, 34.28% and 32.64%, respectively. The control system showed a good control performance.