BP Neural Network Training Research Articles

Application of Visual Recognition Based on BP Neural Network in Architectural Design Optimization.

In order to establish the mapping relationship between architectural design parameters and building performance and optimize architectural design parameters, an architectural design optimization method based on BP neural network is proposed. The selected main design parameters of building ventilation include spacing coefficient, air outlet area, and height from the bottom of the window sill to the ground. Take the comprehensive performance of building ventilation design as the main optimization objective to optimize the building design. First, nine groups of samples of building optimization design are obtained through uniform experimental design. Then, based on the architectural design sample data obtained by BP neural network training, the mapping relationship between architectural design parameters and building performance is established, and based on this mapping, the optimal design parameters of the building are calculated. The research results have a certain reference value for architectural design optimization.

The comprehensive study of the urbanization development and environmental damage response mechanism

With the acceleration of economic globalization and trade liberalization, environmental issues have attracted more and more attention from the world and become one of the major issues facing mankind. A series of global environmental problems that have emerged under the background of economic globalization have become barriers restricting the sustainable development of the economy and society. As one of the key issues in network research, BP neural network provides an effective tool for studying the multi-objective and multi-dimensional interaction between networks. During training, the BP neural network can adaptively memorize the learning content in the weights of the network through learning. That is, the BP neural network has a high self-learning and self-adaptive ability.This paper fully contrasts and studies several neural networks and deep learning models at this stage,This paper fully contrasts and studies several neural networks and deep learning models at this stage, and forms a new type of network model. In this paper, through the construction of BP neural network training model for experimental simulation, the experimental results show that our city's urban expansion phenomenon in 2019 is still showing a growth momentum. The urbanization and eco-environment system of our city are developing in a good level with fast and stable development rate, which indicates that the development under the existing development conditions can realize the coordinated development of urbanization and environment. In order to protect the damaged ecological environment, it is necessary to improve the path of ecological environment damage compensation responsibility and diversified ecological environment damage compensation relief procedures. Academic and legal practitioners can distinguish clearly from the elements of tort construction and environmental tort, improve the system of environmental tort liability, and strengthen China's environmental legislation.

Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network

As the key bearing part of the crane, the low-speed hub bearing of the crane exists in special working conditions of low-speed and alternating heavy load. It is difficult to extract its fault characteristics accurately by existing analysis methods. The main idea of the broadband mode decomposition (BMD) method previously proposed is to search in the association dictionary library containing broadband and narrowband signals. However, when it is applied to the broadband signals interfered by strong noise, the decomposition is easy to produce modal confusion, so the modulated broadband mode decomposition (MBMD) method is proposed. The fault signal just can be analyzed by MBMD, so it is applied to the fault diagnosis of low-speed hub bearing of the crane. To realize the fault identification of low-speed hub bearing of the crane, firstly, the original signal is decomposed by MBMD. Secondly, the eigenvalues of the first three-component signals are calculated, the eigenvalue matrix is constructed, and the marked features are selected by the distance evaluation technique (DET). Finally, the marked features are input into BP neural network for training and testing to identify the types of bearing fault. Compared with EEMD, VMD, and BMD, the MBMD method combined with BP neural network has good performance in feature extraction and fault identification.

Impact Load Identification Algorithm of Helicopter Weapon Pylon Based on Time-Domain Response Signal

Accurately identifying the peak value of impact load acting on the helicopter structure during weapon launch is of great significance to the design and finalization of weapon pylons. Firstly, a method of standardized preprocessing load signal is proposed by analyzing the vibration response and the characteristics of the impact load. Then, the test model of the weapon pylon is designed, and the position of the strain gauge is determined; the static load calibration test and the ground impact test are carried out on the test model. Next, the time-domain response measured by the strain gauge is filtered and de-noised. Impact load is processed by a standardized method. The response and load are used to train BP neural network and the mapping relationship between response and load is established. The impact load generated by a specific weapon is statistically processed to obtain the normalized average load time history, and the identified standard load is converted back to the original load pattern. Finally, the network that meets the error requirements is tested. Both the standardized pattern and the original pattern have high identification accuracy, which shows that an effective load identification model can be established based on the time-domain response signal and the standardized processed load signal.

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.

Evaluation of Urban Tourism Carrying Capacity Based on Analytic Hierarchy Process Optimizing BP Neural Network.

With the rapid development of today's social economy, tourism has also developed rapidly. According to national statistics, from 2017 to 2019, domestic tourism revenue increased from 4.57 trillion to 5.73 trillion. The tourism economy has made more and more contributions to the national economy, and it has also received more and more attention and attention from society. However, in recent years, the “explosive” growth of tourism has not only promoted economic development but also brought some challenges to society and the economy, such as environmental pollution in tourist cities. Therefore, it is of great significance to evaluate the tourism carrying capacity of a tourist destination city to realize the sustainable development of the city's tourism. This article aims to study the evaluation of urban tourism carrying capacity based on AHP and an optimized BP neural network. It designs a carrying capacity evaluation system, conducts BP neural network training for the system, and conducts system testing. The results show that the proportion of scientific and technological innovation is obviously higher than that of other aspects in the proportion of carrying capacity indicators in various aspects of each city. Environmental carrying capacity indicators can be divided into resource supply indicators, pollutant containment indicators, and social impact indicators. This article divides the important indicators into economic development, technological innovation, potential competition, environmental support, and development guarantee. Its indicators account for about 50%, with an average of more than 40%. This shows that the system can clearly display the main factors and evaluation indicators that affect the urban tourism carrying capacity and has certain feasibility and reliability.

Determining application volume of unmanned aerial spraying systems for cotton defoliation using remote sensing images

The Unmanned aerial spraying systems (UASS), with a precise positioning system and convenient control system, have attracted more attention from researchers and the market. However, the UASS operation parameter setting still depends on the operator, and there is still a gap between the intelligent spraying. The upgrade of UASS from automation to intelligence requires a “scientific brain” to make spraying decisions. In this study, the UASS equipped with centrifugal nozzles was used to simulate defoliant spraying, combined with RGB and multi-spectral cameras to collect remote sensing images of the target area. The droplet distribution data were obtained through two years of field trials in two places. A droplet distribution prediction model based on the spray volume of UASS and the remote sensing spectral index that characterizes the cotton canopy structure is established by the BP neural network and Bayesian regularization training algorithm. The cotton defoliant was verified using this model and combined with NY/T 3213–2018 standard. The research results show that it is feasible to use remote sensing images to determine the application volume of UASSs for cotton defoliant. Compared with the conventional application rate and overspray, the defoliant spray based on the decision model can achieve the expected cotton defoliation effect and reduce the application volume.

A Cloud Computing-Based Intelligent Forecasting Method for Cross-Border E-Commerce Logistics Costs

Aiming at the problems of poor forecasting effect and low accuracy and low efficiency in current cross-border e-commerce logistics cost prediction methods, a cloud computing-based intelligent method for cross-border e-commerce logistics cost prediction is proposed. Analyze cloud computing concepts, characteristics, and service models, study cloud computing-related technologies, and train BP neural network algorithms based on BP neural network principles. The BP neural network structure is obtained by determining the number of neurons in the input layer, the number of neurons in the hidden layer, the number of neurons in the output layer, and the activation function of the neural network. Normalize the input data samples of the input layer, and select the initial weight, threshold, and learning rate parameters of the BP neural network to determine the momentum coefficient. This paper uses neural network model combined with Spark cloud computing platform to realize the intelligent prediction of cross-border e-commerce logistics cost. This method has good predictive ability. After a large amount of data input and output relationship training, it has obtained the most suitable model for prediction. The experimental results show that the cross-border e-commerce logistics cost prediction effect of the proposed method is good, and it can effectively improve the accuracy and efficiency of cross-border e-commerce logistics cost prediction.

A Machine Learning Study on Internal Force Characteristics of the Anti-Slide Pile Based on the DOFS-BOTDA Monitoring Technology.

Long-term monitoring of constructed anti-slide piles can help in understanding the processes by which anti-slide piles are subjected to the thrust of landslides. This paper examined the landslide control project of Badong No. 3 High School. The internal force of an anti-slide pile subjected to long-term action of landslide thrust was studied by Distributed Optical Fiber Sensing (DOFS) technology. The BP neural network was used for model training on the monitored strain values and the calculated bending moment values. The results show the following: (1) The monitoring results of the sensor fibers reflect the actual situation more accurately than steel rebar meters do and can locate the position of the sliding zone more accurately. (2) The bending moments distributed along the anti-slide pile have staged characteristics under the long-term action of landslide thrust. Three stages can be summarized according to the development trend of the bending moment values. These three stages can be divided into two change periods of landslide thrust. (3) The model produced by the BP neural network training can predict the bending moment values. In this paper, the sensing fibers monitoring over a long time interval provides a basis for long-term performance analysis of anti-slide piles and stability evaluation of landslides. Using the BP neural network for training relevant data can provide directions for future engineering monitoring. More novel methods can be devised and utilized that will be both accurate and convenient.

Design Optimization of Curved Curtain Wall Based on SPEA II‐GA‐BPNN Algorithm

The optimization of the design of the curved curtain wall to reduce the construction cost can bring about huge economic benefits to the curtain wall project. This paper breaks through the current practice of cost optimization only in the detailed design stage. Based on the characteristics of optimization objectives in different stages, a composite scheme process is proposed, which uses the combination of SPEA II multiobjective algorithm and GA single‐objective algorithm for design optimization. It predicts and evaluates the optimization scheme by BP neural network. The panel meshing scheme is first optimized in the scheme design stage using the SPEA II multiobjective algorithm. The panel meshing scheme is first optimized in the scheme design stage using the SPEA II multiobjective algorithm. The best optimization results are then screened using the GA single‐objective algorithm to optimize the panel type in the detailed design stage. Afterwards, BP neural network training samples are randomly generated in all the optimization schemes, and the BP neural network is trained. The trained BP neural network model is used to predict and select the optimal solution in the solution set. Finally, the initial solution, existing optimization solution, and compound optimization solution are compared and analyzed. The obtained results show that the proposed composite optimization scheme can effectively reduce the cost of panel and keel materials by almost 10%. It can provide new ideas and methods for the design of curved curtain walls.

Model Construction of College Students' Entrepreneurial Ability Cultivation in Mental Health Education Environment.

National policies have been very supportive of college students as a new force in IEE (innovation and entrepreneurship education). Universities across the nation have increased their emphasis on entrepreneurship education and put a lot of effort into developing and enhancing college students' EA (entrepreneurship ability). An important challenge facing universities is how to develop the psychological quality of IEE in college students and create a long-term training system. The author develops the EA evaluation model for college students based on practical experience with mental health education and applies BPNN (BP neural network) theory to the assessment of IEE in higher education. The scores corresponding to the students' evaluation indices are used as input vectors, and the innovative EA of the students is evaluated. According to the simulation's outcomes, the number of hidden neurons is calculated, and the outcomes are assessed through experiments. The outcomes demonstrate that, following the completion of the enhanced BPNN training, the actual value is valid, the expected value is reasonable, and the maximum relative error between them is 1.6 percent, which is essentially the ideal situation. It has been established that the IEE's established evaluation index system is fair and can accurately capture students' IEE spirit.

Design of English Learning Effectiveness Evaluation System Based on K-Means Clustering Algorithm

English is the universal language of the world. In the context of global economic integration, English learning is not only an essential course for business elites but also a required course for the general public. Currently, in colleges and universities across the world, English is presented as a compulsory first foreign language course. Therefore, how to improve the effect of English performance assessment in the context of smart teaching has become an important part of smart English teaching. Due to the influence of interference factors, human factors, or external factors, the traditional English language teaching evaluation system has the problems of high system sensitivity, long envelope delay jitter time, and short stationary state maintenance time. Therefore, this study develops an English learning effectiveness evaluation system based on a K-means clustering algorithm. The SQL Server 2005 database management software is used to develop the system database; various functional modules of the system are designed using ActiveX, with emphasis on the design of scoring functional modules; and different roles and permissions are given to administrators, teachers, and students. A student English learning effectiveness evaluation model based on BP neural network training and K-means clustering algorithm is designed to optimize the English learning effectiveness evaluation model and achieve effective English learning by solving the consistent estimate of the effectiveness of English learning assessment. The performance test results show that the proposed system has a lower sensitivity coefficient, a shorter envelope delay jitter time, and a longer period of steady-state maintenance, indicating that the system can achieve stable operation.

Research on PMF Model Based on BP Neural Network Ensemble Learning Bagging and Fuzzy Clustering

Probability matrix factorization model can be used to solve the problem of high-dimensional sparsity of user and rating data in the recommender systems. However, most of the existing methods use the user to model the item rating, ignoring the relationship between the user and the item, so the accuracy of user-item rating prediction is still low. Therefore, this paper proposes a probabilistic matrix factorization model based on BP neural network ensemble learning, bagging, and fuzzy clustering. Firstly, the membership function of fuzzy clustering and the selection of cluster center are used to calculate the user-item rating matrix; secondly, BP neural network trains the user-item scoring matrix after clustering, further improving the accuracy of scoring prediction; finally, the bagging method in ensemble learning is introduced, which takes the number of user-item scores as the base learner, trains the base learner through BP neural network, and finally obtains the score prediction through the voting results, which improves the stability of the model. Compared with the existing PMF models, the root mean square error of the PMF model after fuzzy clustering is increased by 9.27% and 3.95%, and the average absolute error is increased by 21.14% and 1.11%, respectively; then, the performance of the first mock exam is introduced. The root mean square error of the ensemble method is increased by 4.02% and 0.42%, respectively, compared with the existing single model. Finally, the weights of BP neural network training based learner are introduced to improve the accuracy of the model, which also verifies the universality of the model.

Rolling Element Bearing Fault Diagnosis for Complex Equipment based on MFMD and BP Neural Network

A new fault feature extraction method for rolling element bearing is put forward in this paper based on modified Fourier mode decomposition (MFMD) and multi-scale permutation entropy, and the fault pattern recognition is studied by combining BP neural network. First, introduce frequency band entropy (FBE) to optimize the boundary frequency search and effective modal component selection of Fourier decomposition. Then, use MFMD to adaptively decompose the original vibration signal to several Fourier intrinsic mode functions (FIMFs) and select effective FIMFs. Next, use multi-scale permutation entropy to quantify the fault features. Finally, input the fault feature vector into the BP neural network for fault diagnosis model training and testing. The method proposed in this paper is applied to the rolling element bearing simulation data and the public bearing fault test data set of Case Western Reserve University to verify the feasibility of the method. The results show that the method proposed in this paper has a high identification accuracy for different types of faults, reaching over 98%. The feasibility and superiority of the rolling element bearing fault diagnosis method based on MFMD and BP neural network are verified, and technical support is provided for the health state assessment of rolling element bearings.

Human Error Evaluation Model of Civil Aviation Maintenance Based on Grey Wolf Optimizer

Today, with the rapid development of science and technology, human error has already surpassed mechanical failure and become the most important factor in safety accidents. In order to reduce the occurrence of human error in civil aviation maintenance, and to improve the quality of maintenance production, four levels and 18 factors affecting human error in civil aviation maintenance are proposed in this paper,Taking Hongqiao base of Eastern Airlines as an example, a questionnaire survey was used to collect data,and an Inertial Adaptive Hybrid Gray Wolf Optimization (IAHGWO) was proposed,which combined with particle swarm optimization (PSO) and three improved strategies,and an evaluation model was constructed to use IAHGWO to train BP neural network. The results show that the evaluation model has good practicability and accuracy.

Claim Amount Forecasting and Pricing of Automobile Insurance Based on the BP Neural Network

The BP neural network model is a hot issue in recent academic research, and it has been successfully applied to many other fields, but few researchers apply the BP neural network model to the field of automobile insurance. The main method that has been used in the prediction of the total claim amount in automobile insurance is the generalized linear model, where the BP neural network model could provide a different approach to estimate the total claim loss. This paper uses a genetic algorithm to optimize the structure of the BP neural network at first, and the calculation speed is significantly improved. At the same time, by considering the overfitting problem, an early stop method is introduced to avoid the overfitting problem. In the model, a three-layer BP neural network model, which includes the input layer, hidden layer, and output layer, is trained. With consideration of various factors, a total claim amount prediction model is established, and the trained BP neural network model is used to predict the total claim amount of automobile insurance based on the data of the training set. The results show that the accuracy of the prediction by using the BP neural network model to both the data of Shandong Province and to the data of six cities is over 95%. Then, the predicted total claim amount is used to calculate premiums for five cities in Shandong Province according to credibility theory. The results show that the average premium of the five cities is slightly higher than the actual claim amount of the city. The combination of BP neural network and credibility theory can perform accurate claim amount estimation and pricing for automobile insurance, which can effectively improve the current situation of the automobile insurance business and promote the development of insurance industry.

Neural Network Optimal Routing Algorithm Based on Genetic Ant Colony in IPv6 Environment.

The traditional IPv6 routing algorithm has problems such as network congestion, excessive energy consumption of nodes, and shortening the life cycle of the network. In response to this phenomenon, we proposed a routing optimization algorithm based on genetic ant colony in IPv6 environment. The algorithm analyzes and studies the genetic algorithm and the ant colony algorithm systematically. We use neural network to build the initial model and combine the constraints of QoS routing. We effectively integrate the genetic algorithm and ant colony algorithm that maximize their respective advantages and apply them to the IPv6 network. At the same time, in order to avoid the accumulation of a lot of pheromones by the ant colony algorithm in the later stage of the network, we have introduced an anticongestion reward and punishment mechanism. By comparing the search path with the optimal path, rewards and punishments are based on whether the network path is smooth or not. Finally, it is judged whether the result meets the condition, and the optimal solution obtained is passed to the BP neural network for training; otherwise, iterative iterations are required until the optimal solution is satisfied. The experimental results show that the algorithm can effectively adapt to the IPv6 routing requirements and can effectively solve the user's needs for network service quality, network performance, and other aspects.

Feature Extraction of Laser Welding Pool Image and Application in Welding Quality Identification

Most of the existing laser welding quality identification methods are post-weld identification or low-speed identification (Welding speed below 120m/min). Efficiently online monitoring of laser welding can take the advantages of laser welding for high-speed and deep-penetration welding. How to eliminate interference information (such as metal vapor, plasma splash, etc.) in the laser welding process, accurately and quickly extract the feature information of welding quality evaluation, and identify defects is a major problem that laser welding online monitoring technology needs to solve urgently. In this paper, the optimized dark channel prior anti-interference processing algorithm can remove the interference of image. The feature information extraction algorithm based on contour and OTSU threshold segmentation are used to extract the features of the welding image that collected by the image acquisition system. Then, the image is classified as a specific defect by the trained BP neural network classification algorithm. Experiments with 304 stainless steels have proved that this method can effectively remove the interference of metal vapor and plasma splash on the feature information, and achieves 97.18% accuracy rate of the binary classification test and 91.29% accuracy rate of the six-classification test. The processing time of the entire algorithm is about 0.3ms and it can meet the real-time requirements of high-speed laser welding.

Human Posture Recognition Method Based On Skeleton Vector With Depth Sensor

This paper proposes a human body gesture recognition method. The method collects bone information of the human body through the Kinect depth sensor, and then uses the direction cosine method for feature extraction.Finally, the feature vector is sent to the BP neural network for training and recognition. In the case of less sample training, the system can still accurately identify the five postures of standing, sitting, leaning forward, leaning backward and underarm. The input is composed of low-dimensional bone information, so the speed and recognition response speed during network training is better than the conventional RGB image recognition method. Experiments show that the recognition rate and real-time performance of the system can meet the needs of practical applications.

Road Resource Optimization and Restructuring Based on BP Neural Network

In order to solve the problem of uneven distribution of road resources during rush hour of commuter traffic in cities, a method called “tidal lane” was designed to optimize and restructure the road resources after calculation and measurement of the road occupation rate and the speed of the vehicle. The algorithm based on BP neural network is designed to judge whether the road is changing lanes or reversing with the input of road occupation rate and vehicle speed. The signal is controlled according to the results obtained by the algorithm to achieve the optimization and restructuring of road resources. After the simulation, the trained BP neural network is obtained, and then the test results are obtained. Finally, the correct rate of the network is calculated to reach 97%, which meets the design requirements and proves the reliability and correctness of the method.