BP Neural Network Technology Research Articles

Railway Signal Electronic Control System Based on BP Neural Network

Railway signal system is the core system of railway command train operation. After the installation of the system equipment is completed, a comprehensive test shall be carried out through joint debugging and joint testing, and the applicability of the system to the design requirements shall be generally evaluated according to the test results. This paper focuses on the research of railway signal electronic control system. Based on BP neural network, fault information can be quickly and accurately identified in a relatively short time, and accurate electronic signal control can be realized in a complex system environment. The final result of the study shows that the difference in delay error of railway signal transmission under BP neural network technology is small, there are some subtle differences in 5 different modes, but the impact is small, in mode 1, the target time value is 0.236s, The actual reflection time is 0.322s, it can be seen that the error is controlled within 0.2s, which is feasible. Through the analysis of delay error of railway signal electronic control system, BP neural network has certain advantages in railway signal transmission.

Reform of educational management work in higher education based on departmental sociology in the context of intelligent technology

Abstract Based on Apriori algorithm and BP neural network technology, this paper deeply integrates association rules and data mining technology in education and informatization management, which provides a new method and a new way of thinking for reforming education management work in the context of intelligent technology. In real-life settings, education management is being managed using this new method. Taking the university district of DN201 as the research object, the allocation level of basic education resources in the university district is measured and optimized in a balanced way through indicator analysis. The parameter settings described allowed for the search of strong correlation rules for the mastery level of students in each course cluster. The results show that SN201 has the highest level of educational resource allocation at 8.32. SN215 is at the lowest level at 2.62. This shows that there is an imbalance in its resource allocation. The coefficient of variation of educational resource allocation of SN217 is 0.83 before optimization, and after optimization, the coefficient of variation is 0.65, and the variation becomes lower. The courses have a higher level of enhanced strong association than 4.

Retracted: Optimization of Environmental Parameters of Ice and Snow Sports Venues Based on the BP Neural Network and Wireless Communication Technology

Retracted: Optimization of Environmental Parameters of Ice and Snow Sports Venues Based on the BP Neural Network and Wireless Communication Technology

Risk Management of Supply Chain Green Finance Based on Sustainable Ecological Environment

Green supply chain finance is a new financing method that focuses on corporate restructuring and promotes corporate capital flow and the development of environmental protection. This paper used BP neural network technology to study the green financing of the supply chain under the sustainable ecological environment. The method played an important role in the trial. Due to the more uncertain factors faced and the more complex environment, the risks of green supply chain finance are more hidden, diverse, and complex. The BP neural network is relatively mature in both network theory and performance. Its outstanding advantages are its strong nonlinear mapping ability and flexible network structure. The positive effect of BP neural network on green financial risk management is verified by experiments. Green supply chain finance is an innovative model of green finance. This experiment studies the risk management of green finance in supply chain and the evaluation index of green finance risk management through BP neural network method, and shows that the evaluation results are highly scientific. In addition, based on the green supply chain model, the historical data of different regions provide a scientific basis for the sustainable ecological development of the region. This paper provides guidance for the sustainable development of green finance in the supply chain and makes contributions to promoting the development of green economy. In order to control the risks of supply chain financing business, the risks of supply chain financing business are classified and analyzed, and specific project risk levels and points are determined to propose control measures to ensure effective control of the business risks.

Retracted: Research on Piano Performance Optimization Based on Big Data and BP Neural Network Technology.

[This retracts the article DOI: 10.1155/2022/1268303.].

Retracted: Innovation of Platform Economy Business Model Driven by BP Neural Network and Artificial Intelligence Technology.

[This retracts the article DOI: 10.1155/2022/3467773.].

Teaching Reform of Ancient Literature Based on Credible BP Neural Network Technology in New Media Environment

Recent years have seen a progressive integration of the new media environment into people’s lives, studies, and employment, which has had a profound impact on China’s social economy. It is unavoidable to conduct the educational reform of classical literature in the age of new media. Data mining (DM) is a method for analysing and learning from data in databases and data warehouses using artificial intelligence. They work better together to create a solid foundation for decision-making analysis of businesses or pertinent departments in many fields. The BP algorithm can cause the weight to converge to a specific number, but it cannot ensure that that value is the error plane’s overall minimum. In order to correct the flaw that the BP neural network is prone to falling into local minima, the improved credible BP neural network used in this paper adopts the momentum factor. The findings demonstrate that there is little variation between the predicted value and the actual value achieved by applying the BP algorithm in the experimental group with order of magnitude standardised operation, and that this algorithm’s error has been decreased by 7.38%. It demonstrates that by examining students’ test scores in a data warehouse with a neural network algorithm in DM, we may discover potential patterns among the data.

Product Innovation Design Method Based on BP Neural Network

Innovative product design is the core problem of modern industry and the source power of enterprise development. With the continuous improvement of China’s material level, product innovation design has changed from functional needs to emotional needs, and the product design method centered on users’ emotional needs has attracted much attention. With the emergence of new technologies such as artificial intelligence and big data, the automation and intelligence of advanced algorithms and innovative design methods for online product decision driven by multimodal multimedia data in e-commerce have become inevitable trends in the field of product design, and it is a big challenge to improve the innovative design ability of products by using information technology. In order to solve this problem, based on the research of BP neural network technology, this paper puts forward an innovative product design method based on image-Kempi method. The color features of images are reconstructed and integrated, and then transferred to product modeling, so as to generate new products with both content image modeling features and style image color features, which brings visual inspiration to users. The results show that KENPI method combines the theories of Kansei engineering, convolutional neural network, and neural style transfer, and establishes a mapping model between product modeling elements and product semantics. Convolutional neural network and neural style transfer model are used to extract, reconstruct, and integrate the color features of style diagrams, and transfer them to product modeling to design new product images. By evaluating the image quality and comparing the product semantics before and after the migration, the validity and feasibility of the migration are verified. Based on BP neural network, a nonlinear mapping model between product attribute space and product semantic space is constructed, and the generalization ability of the model is evaluated, which verifies the feasibility and effectiveness of this method. The research results have important theoretical guiding significance for improving the innovative design ability of enterprises, enhancing product competitiveness, and customer satisfaction.

CARACTERÍSTICAS DAS ALTERAÇÕES FISIOLÓGICAS NO TREINAMENTO DE ATLETAS COM BASE EM ALGORITMO DE MINERAÇÃO DE DADOS

ABSTRACT Objective: In the competition of athletic training, it is imperative to use various physiological and biochemical indicators to study the changes they can bear. Methods: In this paper, national tennis players’ physiological and biochemical indicators are taken as samples, and Artificial Neural Network (ANN) in data mining algorithm is used to classify and predict the sample data. Based on this, to solve the BP neural network's failure in easily falling into a local minimum, the ant colony optimization (ACO) algorithm was introduced to train the changes in the neural network. Finally, the improved BP neural network technology of the ant colony optimization algorithm is used in the model to analyze the physiological changes in tennis players. Results: The research results show that the model successfully predicted the physiological change in athletes and could provide coaches with a basis for decision-making. Conclusions: The physiological change in athletes is combined with the neural network algorithm to establish a connection between the two, which provides an effective and reliable method for detecting the physical function of sports transportation with unique guidance in athletes’ training and competition. Level of evidence II; Therapeutic studies - investigation of treatment results.

A Study on the Method of Press Freedom Value Assessment Based on Artificial Neural Network

Freedom of the press embodies an increasingly important role in modern democratic society, and people pay more and more attention to the value of freedom of press. The value of freedom of press has been elaborated from the basic concept, right attributes, and role functions of freedom of press, which are the basis for the existence of the value of freedom of the press and the jurisprudential basis for strengthening its legal construction. Freedom of press is a right extended from the freedom of speech and publication and is the application of freedom of speech and publication in news communication activities. Freedom of the press in the modern sense is considered an institutional right, both a fundamental right and a political right. As the role of press freedom in the process of modern democratic society becomes more and more prominent, the conflict between its value and other social values becomes more and more obvious. In such a context, this study introduces BP neural network technology into the assessment of press freedom value, expecting that a set of assessment method models with press freedom value as the core can be constructed. In order to achieve this goal, the core problem is how to use BP neural network technology to automatically evaluate the value of freedom of the press. A web crawler is used to crawl news from various online sources and the news are scored and evaluated by the system to present the most valuable news first to the readers. A dataset of 1440 data samples was created to be used in the experiments. The proposed GRU with multifeature fusion method has a higher accuracy and lower error rate as compared to LSTM, linear regression, and random forest-based model.

Innovation of Platform Economy Business Model Driven by BP Neural Network and Artificial Intelligence Technology.

In order to enhance the competitiveness of enterprises, how to evaluate and enhance the competitiveness of B2B e-commerce enterprises and promote the orderly and healthy development of B2B e-commerce industry are discussed. This paper puts forward the research on the innovation of platform economic business model driven by BP neural network and artificial intelligence technology. BP neural network is used to study and evaluate the competitiveness of B2B e-commerce companies. According to the B2B e-commerce company competitiveness theory and BP neural network algorithm, combined with BP neural network and B2B e-commerce company competitiveness evaluation index system, a BP neural network model is designed to analyze the competitiveness of B2B e-commerce enterprises. Determine the expected value of network samples, select G1 method to determine the subjective weight, and select entropy weight method to determine the objective weight. With the help of the function in the MATLAB neural network toolbox, the neural network is trained. The results show that when the training times reach 3297 times, the sample mean square error is 9.9869e − 06, and the training network reaches convergence. The samples of three enterprises test the trained neural network and input the data of three test samples into the trained BP neural network, and the output results are 0.1531, 0.1371, and 0.1557, respectively. The network model constructed in this paper is effectively close to the training samples. The established BP neural network has good performance and can be used to evaluate the competitiveness of B2B e-commerce companies. Accelerate technological change and realize innovation. Technological capability is the inexhaustible driving force for the development of enterprises. Only with the innovation of keeping pace with the times can application-oriented e-commerce enterprises meet the needs of customers and the market, form the difference between goods or services, and then enable enterprises to win more customers and market share.

Collaborative optimization method of transportation planning and land use and its system unit decision making

This paper proposes a collaborative optimization method for transportation planning and land use. This method combines system dynamics models, structural equations, DEA evaluation and BP neural network technology to construct a complete transportation planning and land use collaborative optimization system. In this system, system unit determination is a key problem, which has a different relationship with each stage. This paper uses the DEMATEL model to construct a causality diagram and an incidence matrix for the system units, to identify the central influencing unit and causal influencing unit of the method of collaborative optimization of transportation planning and land use. While simplifying the model through the DEMATEL model, the macro interaction mechanism between urban traffic and land use will be clarified. Further, the extracted key influence units can be micro-decomposed to construct subsystems of micro-elements, which makes preliminary preparation for the analysis of static and dynamic integration model.

Wireless Sensor Technology-Based Physical Education Teaching Evaluation.

A wireless sensor network (WSN) is a group of geographically scattered and specialized sensors to monitor and record variables related to environmental and storing the obtained data in a vital location. These networks have applications and can be utilized in different research domains including physical education where error prediction is assumed as one of the core issues. Thus, careful attention is required from the researcher to provide reliable and accurate prediction models. Thus, aiming the shortage of large prediction error in the physical education evaluation, which is based on the BP neural network and wireless sensor technology, a combination of AFP and questionnaire survey method is proposed in order to improve the accuracy and predictability of evaluation, according to the characteristics of different evaluation subjects. We select the evaluation index system as the input of wireless sensor technology and then use the principle of genetic algorithm to select the optimal individual and optimize the initial parameters of wireless sensor technology to establish the evaluation model of physical education quality. Through the training and testing of sample data, it is shown that the model greatly improves the accuracy of physical education quality evaluation and has a good application prospect in physical education evaluation.

Sales Forecast of Marketing Brand Based on BP Neural Network Model.

With the advancement of globalization, the market competition among enterprises has become increasingly intense. To win a good market, an enterprise must understand and grasp the laws of the market economy and accordingly predict the future of the market. Efficient market estimates are based on a careful study of various types of market data. Therefore, enterprises must engage in preliminary research and data collection, based on a complete data system, and ensure the accuracy of vision predictions by developing a scientific market vision. Only by ensuring correct estimates can companies develop a right business plan and ultimately capture the market. More traditional sales forecasting methods generally only involve some details of sales, not accounting for relatively complex interactions among those factors (price, consumer income, etc.) that affect demand, and as a result, the models built are relatively simple. Artificial neural networks have excellent capabilities for infinite mapping and passive learning. This affects the requirements among the various factors, as well as the more complex relationships between them. In terms of weights, it is safe for neural networks. Therefore, BP neural network technology is used by most people to predict the number of sales, and a more coherent sales forecast method has been established for this purpose. Predicting sales targets is a very complex process, as the experimental results show. The prediction accuracy of this model is much higher than that of other common prediction methods. Its prediction accuracy is more than 30% higher than that of conventional methods, and it also has better comprehensive performance. This has a certain application value for sales forecasting work.

A Prediction Model of Human Resources Recruitment Demand Based on Convolutional Collaborative BP Neural Network.

This paper presents an in-depth study and analysis of the prediction model of force resource recruitment demand using a convolutional neural network combined with a BP neural network algorithm. BP neural network technology is introduced to be applied to enterprise management talent assessment activities. Using BP neural network has strong parallel processing characteristics, as well as unique adaptive learning and feedback adjustment capabilities while combining the traditional enterprise talent assessment system, to build a business management talent assessment model based on BP neural network technology, to circumvent the possible influence of subjective factors in talent assessment, reduce assessment errors, and improve the accuracy and validity of the assessment. The first layer of convolutional layers may only extract some low-level features such as edges, lines, and corners, and more layers of the network can iteratively extract more complex features from low-level features. The constructed applicant reputation evaluation model based on multiplicative long- and short-term recurrent neural network and the hybrid project recommendation model based on conditional variational self-encoder were experimented on Freelancer's dataset for effectiveness, respectively, and the experimental results showed that the proposed employer hiring decision model, reputation analysis model, and applicant project recommendation model have more reliable performance compared with the existing models. The research results achieve more efficient matching of labor supply and demand in the online labor market and provide technical support for the online labor market platform to realize personalized, intelligent, and accurate services for both employers and applicants.

Injuries in College Basketball Sports Based on Machine Learning from the Perspective of the Integration of Sports and Medicine.

Basketball is one of the popular sports in colleges. Basketball injuries are a common thing, and the use of machine learning and other technologies can effectively reduce basketball injuries, which should start with prevention. Nonstandard basketball movements and lack of physical coordination will not only reduce sports efficiency for athletes but also increase the probability of injury. Therefore, effective reduction and targeted prevention of nonstandard actions are of great significance to college basketball. With the development of science and technology, artificial intelligence technology is closer to our lives. Based on the machine learning platform, this paper studies basketball injuries from the perspective of the integration of sports and medicine. Research on what aspects cause college students' basketball injuries is needed for the future. Effectively preventing college students from being injured in basketball is an urgent problem in the field of sports medicine. To find the most suitable machine learning platform for college basketball injury research, this article will introduce three different methods for comparative analysis. The techniques used in the experiment in this paper are traditional BP neural network technology, SCG neural network technology, and RBF neural network technology. Through experiments, it is known that, through experiments, RBF neural network technical prediction accuracy rate is as high as 95.4%, which is a relatively good neural network algorithm for studying the basketball loss of college students.

Optimization of Environmental Parameters of Ice and Snow Sports Venues Based on the BP Neural Network and Wireless Communication Technology

Ice and snow sports have become a scene of attraction nowadays. It not only encourages sports but also earns more revenue through tourism. Tourists love and admire this kind of sport because of its uniqueness, unlike other sports. The Olympic Games also conduct ice and snow sports under the Winter Olympic Sports. This kind of sport is played in ice rinks. Ice rinks are of two types, namely, natural and artificial. Natural ice rinks are formed in snowy regions, whereas artificial ice rinks can be set up anywhere with the help of technology. An artificial or natural ice rink, a sports venue, or a stadium is constructed accordingly. This research focuses on the optimization of environmental parameters of the ice and snow sports venue based on the BP neural network and wireless communication technology. There are various factors to be monitored when an indoor ice and snow sports venue is being considered. They are temperature monitoring, health monitoring of the audience, natural disaster detection and alarm in the case of natural ice rinks, and building safety concerning the climatic conditions. The proposed methodology uses BP neural networks and wireless communication technology to optimize the environmental parameters of the ice and snow sports venue. It has been demonstrated to have numerous advantages over traditional ice and snow sports methods. In this research, the convolutional BP neural network algorithm was implemented to analyse the environmental parameters of the chosen sport. The proposed algorithm is compared with the existing weighted centroid model. The results show that the proposed model has achieved an accuracy of 99.45%.

Research on Piano Performance Optimization Based on Big Data and BP Neural Network Technology.

At present, there are many chess styles in piano education, but there is a lack of comprehensive, scientific, and guiding teaching mode. It highlights many educational problems and cannot meet the development requirements of piano education at this stage. However, the piano scoring system can partially replace teachers' guidance to piano players. This paper extracts the signal characteristics of playing music, establishes the piano performance scoring model using Big Data and BP neural network technology, and selects famous works to test the effect of the scoring system. The results show that the model can test whether the piano works fairly. It can effectively evaluate the player's performance level and accurately score each piece of music. This not only provides a reference for the player to improve the music level but also provides a new idea for the research results and the application of new technology in music teaching. This paper puts forward reasonable solutions to the problems existing in piano education at the present stage, which is helpful to cultivate high-quality piano talents. Experiments show that the application of Big Data technology and BP neural network to optimize the piano performance scoring system is effective and can score piano music accurately. This paper studies the performance scoring system and gets the model after training, which can replace music teachers and alleviate the shortage of music teachers in the market.

A Novel Method of BP Neural Network Based Green Building Design—The Case of Hotel Buildings in Hot Summer and Cold Winter Region of China

With the advent of the big data era, architectural design gradually tends to become more quantified and intelligent. This study proposes a novel green design method for energy-saving buildings based on a BP neural network. This study changed the traditional trial–error mode by evaluating energy consumption based on design performance parameters such as building shape, space, and interface. Instead, energy consumption quota values obtained from statistical data, as well as thermal parameters and energy system parameters in energy-saving standards, were taken as input parameters, and then the design scheme of building shape can be obtained through BP neural network technology. Based on data of 61 hotel buildings in a representative city among a hot summer and cold winter climate zone, the BP neural network model is established to control the building design variables, with 41 kgce/m2·a as its energy-saving design target. Through the energy consumption quota, the trained BP network is applied to predict the optimal architectural design parameters, including the building orientation angle, shape coefficient, window–wall ratio, etc., for twelve building typologies in an area range of 5000~60,000 m2. With recommended control thresholds of quantifiable architectural design elements obtained, this research can provide effective design decision-making suggestions for architects.

Research on Energy Control System of Office Building Based on the BIM and BP Neural Network Technology

As the proportion of building energy consumption in the total energy consumption continues to increase, more and more countries pay attention to the prediction analysis and operation control of building energy consumption. In this paper, an office building energy management and control system based on the building information modeling (BIM) and back-propagation neural network (BPNN) technology is proposed. Firstly, the BPNN algorithm is used to predict the energy consumption of the building, and then the model predictive control (MPC) method is employed to search the optimal control parameters to realize the energy saving of air-conditioning system and other equipment. In addition, the office building environment model is established through the BIM technology, which can real-time display the operating conditions of equipment and more visually control the equipment in the office building. The experimental results show that the system can satisfy the comfort level and significantly reduce the energy consumption.