Application Of BP Neural Network Research Articles

Which airline should undertake a large emission reduction allocation proportion under the "carbon neutral growth from 2020" strategy? An empirical study with 27 global airlines

The “Carbon Neutral Growth from 2020″ strategy (CNG2020 strategy) has predicted the total emission reduction amount but has not arranged the special emission reduction proportion for an individual airline. In this paper, we build a DEA model to calculate the emission reduction allocation proportion and explore the impacts of a different carbon price. We select Number of Employees, Fleet Size, and Aviation Kerosene as then inputs, and set Total Revenue as the output. Then we apply BP neural network to predict the data from 2021 to 2023 based on the actual data of 27 global airlines from 2008 to 2018. We discuss the allocation proportions when the price is 8 dollars/ton, when the price is 14 dollars/ton and when the price is 20 dollars/ton. We have some findings: 1. Skyteam will have the largest average reduction allocation proportion in 2021 and 2023, while Oneworld will have the largest allocation proportion in 2022.2. The largest average ERAP in 2021 and 2023 is from Delta Air Lines, and the largest average ERAP in 2022 is from Air France-KLM.

An Automatic Monitoring Method of Slope Deformation in Open-pit mine based on BP Neural Network and GIS Technology

At present, the research on BP neural network has achieved good results in many industries and fields, but there are few projects in the application research of mineral resources mining. Under the social background of the rapid development of electronic information technology, BP neural network and GIS technology are combined to carry out research and application, which will provide a new research path for slope deformation monitoring and disaster prevention in mining area. Therefore, in the paper, the key technology of open-pit mine slope deformation automatic monitoring based on BP neural network and GIS technology was put forward. Firstly, the advantages of BP neural network were analyzed and BP neural network was selected as the prediction model of slope deformation. The artificial fish swarm algorithm was used to improve the BP neural network to improve the performance of the model. Based on the analysis and construction of GIS technology, the combination application of BP neural network and GIS technology was discussed. Through practice, the application effect of the technology was verified, and it has good theoretical and practical value

Application of BP Neural Network in Early-Warning Analysis of Investment Financial Risk in Coastal Areas

Wang, J. and Xie, S., 2020. Application of BP neural network in early-warning analysis of investment financial risk in coastal areas. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 259–262. Coconut Creek (Florida), ISSN 0749-0208.Financial risks in coastal areas are not only related to the current state of capital markets, but also to the positive fiscal policy effects implemented in recent years. Foreign speculative capital has not yet had a serious impact on China's monetary and financial markets. In this paper, first, the factors of financial risk in coastal areas are identified, and the corresponding evaluation index system is established with the Delphi method. Then the back-propagation neural network model is used to carry out the early-warning analysis of the major destinations of overseas mining investments. The results show that risk warnings will be moderate in Canada, Russia, and Australia in the coming years, with fewer risk warnings in Brazil, India, and South Africa. South Africa's economy is in a poor state of economic development; Brazil's real lending rate is too high. Chinese enterprises may consider investing in overseas mining in countries where the risk warning is light.

Study On The Application Of BP Neural Network In The Prediction Of Office Building Energy Consumption

energy consumption is a big problem that can not be ignored in the development of the world. Energy consumption of building accounts for a large proportion of the total energy consumption. With the development of economy and the improvement of modernization, China’s construction industry is in a stage of rapid development. Therefore, under the strong advocacy of China’s energy conservation and emission reduction policy and sustainable development strategy, the construction industry must also follow this trend. In the future development, it is inevitable to integrate the environmental protection concept including economy, energy conservation and environmental protection. However, the energy-saving technology on buildings in China lags behind compared with developed countries, so based on the pattern of energy consumption on buildings in China, this paper proposes an application scheme of energy consumption prediction of an office building in South China by using BP neural network, in order to produce an available research direction for the prediction of energy consumption of office buildings, and promote it to other types of buildings, and provide relevant data support for building design. It will reduce the investigation cost of building energy consumption and be able to offer some reference for the relevant standards of controlling building energy consumption.

An Integrated quantitative modeling approach for fault-related fractures in tight sandstone reservoirs

Fault-related fractures widely exist in tight sandstone reservoirs, which greatly improve the heterogeneity of reservoir. Unraveling the spatial distribution of fractures is most important for enhancing oil and gas recovery. In this study, an integrated quantitative modeling approach is proposed and applied to the Boxing sag in Jiyang Depression, Bohai Basin, China. From observations of drilled cores and well log interpretations we construct the internal structure of the fault damage zone and establish its geological conceptual model. A single well rock mechanics model is built by applying BP neural network method on rock mechanics experimental data. With the sedimentary facies guided lithofacies model, a three-dimensional heterogeneous rock mechanics model is constructed by integrating three geostatistical methods (i.e. sequential Gaussian simulation method, probability volume modeling method and trend modeling method). This workflow assigns the geological concept and rock mechanics characteristics to the geological volume. Based on the energy conservation principle, the relationship between fracture density and strain energy is derived using different fracture criteria. Based on simulation and calculation results, the fracture density model is established by deterministic modeling. Finally, these properties are incorporated into a DFN model. The model results show that, at the regions near the faults, the fracture development is mainly controlled by faulting. The induced fracture zone is a fracture-intensive area. However, there are few fractures within the fault core zone. The regions far from the faults are mainly controlled by the rock mechanics properties of geological materials, which show zonal characteristics. This model is validated by data from five wells in this region, which shows a strong correlation between predicted fracture density and water cut in produced fluid.

Early Warning Method of Marine Products Network Marketing Risk Based on BP Neural Network Algorithm

Wang, B., 2020. Early warning method of marine products network marketing risk based on BP neural network algorithm. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 177–181. Coconut Creek (Florida), ISSN 0749-0208.In order to reduce the early-warning error of marine products network marketing risk, BP neural network algorithm is used to optimize the early-warning method of network marketing risk. According to the structure of BP neural network, the risk early-warning model of seafood network marketing is constructed, and the risk early-warning indicators are set up under the model. This paper analyzes the mechanism of the network marketing risk. Under the risk early warning model, the BP neural network algorithm is used to train the input value of the network marketing and to judge the risk. Finally, the output results of the algorithm are compared with the set warning level to show the early warning results of seafood online marketing risks. Through the contrast experiment with the traditional early warning method, it is found that applying BP neural network algorithm to the risk early warning method of seafood network marketing can effectively reduce the error by 99.8%.

Reliability Assessment of Distribution Network with Distributed Generation based on BP Neural Network

The distributed power supply is developing rapidly to realize the energy conservation and consumption reduction of the whole society. But the development of distributed generation (DG) will affect the reliable operation of distribution network. A novel reliability assessment model for distribution network with DG was integrated. A series of essential criteria can be extracted by Principal Component Analysis (PCA). And their weights can be obtained based on the entropy method. The assessment results can be calculated by applying BP neural network. Example analysis were implemented to prove the validity of the comprehensive reliability assessment model.

Multi-parameter online optimization algorithm of BP neural network algorithm in Internet of Things service

With the development of science and technology, the application of the Internet of Things (IOT) is becoming more and more widespread. Applying BP neural network algorithms (NNA) to the IOT system will help improve the performance of the IOT system. The research purpose of this paper is to solve the problems of long-parameter measurement cycle and untimely feedback of the existing IOT online measurement system. In this paper, a multi-parameter (MP) IOT online measurement system based on BP NNA is designed, and a simulation test experiment is performed. The MP online measurement IOT system based on the BP NNA completes the parameter collection, analysis, and display through the perception layer, network transmission layer, and application layer. The core is that the system application layer adds the BP NNA to optimize real-time acquisition parameters, processing to reduce parameter measurement time. It can be known from algorithm simulation experiments that the online measurement system based on the BP NNA proposed in this paper uses the BP NNA to predict the absolute error value of the final moisture content and the measured moisture content within 0.3, and the absolute error of the moisture content value in actual production. It is acceptable in the range of 0.5, which speeds up data collection time. This system has a very good effect on improving the feedback adjustment speed of the manufacturing process system.

The Application of Artificial Intelligence Technology in the Quality Evaluation of Dance Multimedia Teaching in Higher Vocational Colleges

Through scientific and effective evaluation of dance classroom teaching quality, teachers can objectively and accurately evaluate teaching methods, attitudes and effects, and lay a cognitive foundation for the improvement of dance teaching quality. The purpose of this article is to evaluate the teaching quality of dance multimedia in vocational colleges based on artificial intelligence technology. The application of BP neural network in teaching evaluation and the algorithm of BP neural network teaching quality evaluation model are studied, and the construction of dance classroom teaching quality evaluation system is discussed. First, the same teacher uses the same teaching materials and uses different teaching methods to teach the students in the two classes respectively, and secondly, an interest survey on dance classes. The experimental results show that multimedia teaching has brought great convenience to teachers in preparing lessons and improved their professional level. In addition, the use of courseware to demonstrate actions in teaching avoids the negative impact on student presentations due to differences in teacher age, technology, and personal understanding. Compared with the control class, the experimental class increased the density of free practice by 10.93%, and the heart rate increased by 8 times / min.

Application of BP neural network in renal pathological prediction in children with hematuria as main clinical manifestation

Objective To explore the differences in clinical indicators of different pathological types of children with hematuria as the main manifestation, and to establish a BP neural network prediction model based on clinical data. Methods The clinical data and renal pathological results of children who were referred to Children′s Hospital of Chongqing Medical University from June 2003 to December 2018 for evaluation of hematuria as the main manifestation were collected, the significant differences in these clinical indicators were analyzed, and a BP neural network model for predicting renal pathology in children with hematuria as the main manifestation was established. Results A total of 438 cases were enrolled in this study, including 232 males and 206 females, with the onset age of (7.00±3.15) years old.According to different clinical manifestations, the children were divided into microscopic hematuria group(179 cases), gross hematuria group(81 cases), microscopic hematuria and proteinuria group (44 cases), and gross hematuria and proteinuria group(134 cases). There were significant differences in sex ratio, onset age, course of disease, inducement, Addis count of urinary red cells, 24-hour proteinuria, blood urea nitrogen, serum creatinine, serum albumin and serum IgA levels among different clinical manifestations (all P< 0.05). Pathological grouping indicated that there were significant differences in sex ratio, onset age, course of disease, family history, Addis count of urinary red cells, 24-hour proteinuria, blood urea nitrogen, serum creatinine, serum albumin, serum IgA and C3 levels among different pathological groups (all P< 0.05). The BP neural network prediction model was then constructed based on the above indicators, and the accuracy of the prediction model was measured to be 61.19% by using the leave one out method. Conclusions By comparing the differences of various indicators under different clinical manifestations and pathological types, a BP neural network prediction model for renal pathology in children with hematuria as the main manifestation is established.The model can accurately predict renal pathology with the help of related indicators, and provides a basis for determining the time of kidney biopsy. Key words: Hematuria; Renal pathology; BP neural network; Prediction

Conventional Bridge Damage Identification Based on BP Neural Network

Aiming at the problem of insensitivity of damage identification in conventional bridges, the application of BP neural network in long-span bridges is studied, and it is extended to the field of damage identification of conventional bridges. The finite element models of three-span continuous variable cross-section box girders under intact and damaged conditions are established by Midas civil, and the eigenvalues of bridges under different conditions are analyzed. It is found that in conventional bridges, the sensitivity to structural damage is: mode > vertical displacement > natural frequency. The parameterized natural frequencies and modes of structures are used as input of BP neural network, and the damage location and degree are used as output to train the neural network. Then, the damage location and degree are identified under different working conditions, and the results show that the recognition effect is not satisfactory. Analyzing the reason, the natural frequency and modal shape of the structure will change when the conventional bridge is damaged, but the deformation value is very small. When training the BP neural network, it is easy to appear over-fitting, which results in poor recognition effect. Therefore, it is difficult to identify structural damage by using BP neural network through acquiring the characteristics of conventional bridges. It is still necessary to study appropriate damage identification parameters and methods that can be applied to conventional bridges.

Application of BP neural network based on genetic algorithm optimization in thermal conductivity of nanofluids

In this paper, a hybrid model (BP-GA) including a back propagation network and a genetic algorithm is used to estimate the thermal conductivity of the nanofluids. The genetic algorithm is used to optimize the initial weight and threshold of BP neural network, so that the optimized BP neural network can better predict the function output. In this study, CuO-ZnO hybrid nanofluids with the mass fraction of 0%, 1%, 2%, and 3% were studied at temperatures of 25, 30, 35, 40, 45, 50, 55, and 60 °C, respectively. The thermal conductivity of the hybrid nanofluids base liquid is 20:80, 40:60, 50:50, 60:40, 80:20, and the mass ratio of CuO-ZnO is 50:50. The mass fraction of CuO-ZnO, the temperature and the mixing ratio of different base liquids were used as input parameters, and the thermal conductivity was the output parameter, forming a 3-input ANN neural network. The predicted thermal conductivity results of BP neural network and genetic algorithm optimized BP neural network (GA-BP) were compared with experimental data. The results illustrate that the BP neural network optimized by genetic algorithm exerts a positive influence on the accuracy and stability of the predicted output.

Application of BP neural network in the analytic hierarchy process of person-post evaluation model

The matching of personnel and management of college administrators is related to the normal operation and development of colleges and universities. In view of the problem that person-post matching is too subjective in decision-making, the weight of each index in the evaluation index system of the manager’s person-post matching is determined by using analytic hierarchy process (AHP) in this paper. Then, evaluation of person-post matching of managers by BP neural network and a person-post matching evaluation model of university managers based on BP neural network is constructed. The experimental results show that the error of person-post matching model is controlled within 5%, and a good evaluation result is obtained. At the same time, it shows that the model can adapt to the expert’s brain thinking to process the sample data, fully absorb the expert’s judgment experience, can scientifically and effectively evaluate the matching of managers and personnel, and provide a new idea for the objective decision-making of the person-post matching problem.

Application of BP Neural Network in Network Fault Diagnosis

There are various deficiencies in network fault diagnosis methods but the need for fault diagnosis is further increasing. In order to adapt to the current development needs, the paper applies the artificial neural network concept to network fault diagnosis. Aiming at the slow convergence speed of traditional neural networks and the tendency to fall into the local optimal solution, first we attempt to add the momentum factor and then adopt rough set preprocessing data. The simulation results show that the improved algorithm has a certain advantages for the original BP algorithm, and it has a certain value for future fault diagnosis research.

Application of BP Neural Network in Digital ImageRecognition

BP neural network is a new image recognition method, mainly in the traditional image recognition method, adding an algorithm about neural network.The method of image recognition using BP neural network is raised in this paper. Rather than feature extraction, image data is directly used as input. At the end, the network training and testing is completed in Python, and the experimental results are given. The results show that the neural network image recognition system without feature extraction has strong anti-interference ability.

The Application of BP Neural Networks to Analysis the National Vulnerability

Climate change is the main factor affecting the country’s vulnerability, meanwhile, it is also a complicated and nonlinear dynamic system. In order to solve this complex problem, this paper first uses the analytic hierarchy process (AHP) and natural breakpoint method (NBM) to implement an AHP-NBM comprehensive evaluation model to assess the national vulnerability. By using ArcGIS, national vulnerability scores are classified and the country’s vulnerability is divided into three levels: fragile, vulnerable, and stable. Then, a BP neural network prediction model which is based on multivariate linear regression is used to predict the critical point of vulnerability. The function of the critical point of vulnerability and time is established through multiple linear regression analysis to obtain the regression equation. And the proportion of each factor in the equation is established by using the partial least-squares regression to select the main factors affecting the country’s vulnerability, and using the neural network algorithm to perform the fitting. Lastly, the BP neural network prediction model is optimized by genetic algorithm to get the chaotic time series BP neural network prediction model. In order to verify the practicability of the model, Cambodia is selected to be an example to analyze the critical point of the national vulnerability index.

Application of BP Neural Network and PSO Methods for Process Design of Cold Rolling Steel

Application of BP Neural Network and PSO Methods for Process Design of Cold Rolling Steel

Fiber Bragg grating temperature calibration based on BP neural network

A novel method which applies BP neural network (BPNN) to temperature calibration of fiber Bragg grating (FBG) sensors is proposed and discussed. Processing and analysis of experimental data showed that this method fitted very well the complex relationship between the center wavelength of FBG and temperature which is approximately linear in room temperature whereas nonlinear in low temperature. The maximum absolute error and root mean squared error were respectively 0.9434 °C, 0.2102 °C in fitting and 0.8943 °C, 0.2081 °C in testing which verified the advantage of BPNN fitting compared with the previous polynomial fitting. The forecasting performance of BPNN was also satisfactory. The novel FBG temperature calibration method based on BPNN has considerable application prospect in FBG temperature measurement.

Risk Management of Airfield Obstacle Free Space Based on Supervised Neural Network

In the application of current airfield clearance standard, operation differences are neglected. As a result, this paper sets up a distributional pattern of flight paths at some airfield and new obstacle limitation standards on various security target levels. It interprets and fits the flight data into actual trajectory curves and applies BP neural network to build a prediction model of the spatial position of aircrafts. Based on this theory, the model is implemented in further statistical analyses of the original and simulation data collected in this airfield, which leads to the establishment of distribution rules of aircraft trajectory. This paper proposes a fresh method to set up an airfield obstacle free space and has important reference values to improve the administration of airfield obstacle free space.

基于主成分分析的BP神经网络在水华预测中的应用

基于主成分分析的BP神经网络在水华预测中的应用