Application Of BP Neural Network Research Articles

Application of BP Neural Network Improved by Fireworks Algorithm on Suspender Damage Prediction of Long-Span Half-Through Arch Bridge

In recent decades, with the large-scale construction and rapid development of half-through arch bridges, as well as the increase of bridge service time, the suspender damage of arch bridge has become increasingly prominent. Therefore, real-time monitoring and regular detection of the health of arch bridge suspenders and timely detection and accurate judgment of the damage location and extent of suspenders are of great engineering significance for evaluating the reliability and residual life of arch bridge structures. By analyzing the main difficulties and existing problems of suspender damage identification, this paper takes the change rate of modal curvature as the damage index, introduces fireworks algorithm into the neural network model, optimizes the optimization process of neural network weight and threshold, and proposes a prediction model based on improved BP neural network by fireworks algorithm. According to the measured data of the damage degree of a long-span arch bridge in daily monitoring and on-site inspection, the proposed prediction method is applied to verify the effectiveness and accuracy in engineering health detection. On this basis, the improved BP neural network by fireworks algorithm is used to predict the suspender damage of a certain long-span half-through arch bridge, which provides an important basis for the actual bridge safety assessment.

An Application of BP Neural Network to the Prediction of Compressive Strength in Circular Concrete Columns Confined with CFRP

The neural network comprises many neurons with extensive interconnections operating parallel and performing specific functions. This paper establishes a BP neural network prediction model for the compressive strength of CFRP-confined concrete based on a large number of experimental data to study the predictive ability of the BP neural network on the compressive strength of CFRP-confined concrete and the output performance of the neural network model. The model is based on a BP neural network that has been trained using many experimental data. An investigation is being conducted on the effect of different data combinations on the accuracy of the predictions made by the neural network model. The high-precision BP network model is created into generic and simplified formulae for application convenience. These formulas are developed based on the theory of neural networks. The neural network models' findings and the empirical formulae for making predictions are compared and discussed. The BP neural network accurately predicts the compressive strength of CFRP-confined concrete, with over 90% of its data points having less than 15% error. In comparison, the regression model shows less accuracy, with less than 70% of its data points having an error within 15%. Compared to traditional regression models, the simple linear equation derived using Purelin instead of Sigmoid as the transfer function only adds a constant term. The average value of prediction/test results is 1.011. The analysis results show that BP neural network can extract the input and output parameters' data information well and obtain a high-accuracy prediction model. The coefficient of variation is 0.112, which indicates that the prediction accuracy and stability are greater than average.

The genetic algorithm and BP neural network in financial supply chain management under information sharing

AbstractIn today's global business competition environment, the significant changes in the market environment make it very difficult for supply chain enterprises to gain a place in the fierce market only by their strength. This article will do the following research work to effectively play the role of supply chain management. First, the concepts, and characteristics, related applications of supply chain information sharing (IS) and genetic algorithm back propagation (GABP) neural networks, are expounded. Then, the feasibility of applying BP neural network (BPNN) to evaluate supply chain IS is analysed. Next, the assessment indicator system for IS is constructed from four aspects: IS infrastructure, IS member characteristics, IS content, and the quality of supply chain enterprises. Finally, the principle and algorithm of BPNN are analysed, the network model is constructed, the BPNN process based on GA is designed, and every step in the process is explained in detail. The sample input is provided for the network model by obtaining and sorting data. The supply chain IS assessment model based on the GABP neural network is constructed. The results show that the assessment model of supply chain IS based on the GABP neural network has high accuracy, and the highest network accuracy reaches 95.01%, which proves that the assessment model based on BPNN has certain advantages for the supply chain IS of the financial industry.

Retracted: Application of BP Neural Network in Matching Algorithm of Network E‐Commerce Platform

Retracted: Application of BP Neural Network in Matching Algorithm of Network E‐Commerce Platform

Application of BP Neural Networks in Tide Forecasting

Tidal phenomenon is a significant dynamical phenomenon in the ocean, and the accurate prediction of tide is an important task for various maritime activities. This paper proposes analysis method considering tidal periodicity and apply it to the actual tide prediction. The results prove that this method can solve the delay problem in tide prediction, improve the accuracy of prediction. Compared with the tidal harmonic analysis method, the prediction result of this method is more accurate and requires less data for short-term tidal forecast. Although this study can only provide an accurate forecast for 3 days, it is enough to deal with risks. How to improve the accuracy of long-term prediction is one of the future research directions.

Optimization prediction and experimental verification of cyclone performance based on BP neural network

As a kind of centrifugal separation equipment, hydrocyclone has developed rapidly with the advantages of high economic benefits, good compactness, high processing efficiency, simple process and so on. Hydrocyclone has broad practical significance and application prospects in chemical, light industry, oil mining and refining, environmental protection, medicine, food processing, ship transportation and surface oil spill treatment. BP neural networks are mainly used in the following four aspects: function approximation, pattern recognition, classification, and data compression. In this paper, we introduce the principle of BP neural network, establish an analytical model, apply BP neural network to predict the performance of hydrocyclone, and conduct experimental verification to optimize the hydrocyclone under different working conditions according to the experimental results.

A Study on the Application of BP Neural Network Based on Visual Recognition in Regional Economic Forecasting

The economic growth in the new normal is no longer limited to the total amount and scale of economic growth in the traditional and neoclassical periods, but has changed to “quality” and “development” under the dual requirements of historical changes and tasks of the times. The quality of regional economic growth is an important part of the quality of China's economic development and an important part of the quality of China's economic development in the new era. Therefore, this paper proposes a BP neural network based on visual recognition in a regional economic prediction model and conducts application experiments. This regional economic forecasting model is relying on data technology for economic panel data mining, then graphical processing of panel data, followed by the selection of visual recognition technology for economic panel map analysis, to derive its various component coefficients, and finally then using the BP neural network to fit the prediction, at the same time, through long-term and short-term prediction, to predict the future development quality of each region's change trends and fluctuations, to predict the institution's role, so as to avoid major transitions and deteriorating alarms, and to provide support for the macroregulation of regional economic development quality.

Application of BP Neural Network in Teaching Quality Evaluation of Higher Vocational Education

Higher vocational education has developed rapidly and attracted wide attention. Teaching quality evaluation is an important part of teaching management in classroom teaching and is an important part of teaching management. Based on the theory of teaching quality, 16 evaluation indicators are identified from the four aspects of teaching attitude, teaching content, teaching process, and teaching results and an HVE quality evaluation index system is constructed. The evaluation model is established by BP neural network algorithm. Finally, five higher vocational colleges in Henan Province are selected for empirical research. The results show that the evaluation model constructed in this paper has certain applicability and can provide a reference for HVE quality evaluation and improvement. Although the evaluation models herein have certain applicable values, further improvements are still needed in terms of indicator identification and correction.

Application of BP Neural Network in Matching Algorithm of Network E-Commerce Platform

In order to solve the matching algorithm problem of network e-commerce platform, a method of applying BP neural network in the network e-commerce platform matching algorithm is proposed. First of all, combined with the actual situation of the platform, select 9 factors that are most in line with the company’s actual business model to influence the selection for analysis; secondly, import 60 sets of data into MATLAB software, measure the input and output data uniformly, and divide the sample data matrix into training set and test. Finally, after multiple factor combinations and verifications, it is concluded that in the training model of the five main factors, the prediction results of the model are compared with the real values. The feasibility of establishing the selection model based on BP neural network is proved. Online e-commerce platforms can refer to this model to build a product selection model that meets the needs of the platform, helping enterprises to achieve more efficient product selection work. Since the parameter initialization of the neural network is random, although the output results are different after the program runs for many times, the R2 is still stable between 0.7 and 1.0, which proves that the predicted value made by the system is highly approximate to the real value and can achieve the predicted effect.

Power Load Forecasting Based on BP Neural Network

Load prediction of power system plays an important role in real-time control and ensuring economic, safe and reliable operation of power system. At the same time, the variation of power load system parameters is affected by many factors, which will lead to the nonlinear development of load curve. In this paper, a prediction method based on BP neural network is proposed. The biggest advantage of this method is that it has adaptive function to a large number of nonlinear characteristics and non-accuracy laws. This paper mainly focuses on the application of BP neural network in short-term load prediction of power system to do further research, and through MATLAB BP neural network design, simulation results show that the application of BP neural network in short-term load prediction is feasible, can better reflect the nonlinear characteristics of load prediction. However, because this paper does not consider the effects of climate, temperature, holidays and other factors, there is still a certain gap between the simulation results and the ideal results, but it is certain that BP neural network is still better than the traditional prediction method, is a very promising new research and development field.

Application of BP neural network in on-board charger fault diagnosis

With the accelerated growth of the number of electric vehicles, the frequent occurrence of electric vehicle charging safety accidents has brought some safety hazards to people. The fault diagnosis of on-board charger circuit has gradually become the focus of attention of scholars. This paper uses simulation software to conduct modeling simulation analysis, failure mode analysis and data acquisition for the on-board charger of electric vehicles, and establishes a fault diagnosis model based on BP neural network, which is applied to the fault diagnosis of on-board charger circuits. The optimal fault diagnosis model is obtained by continuously adjusting the number of neurons in the hidden layer of the BP neural network, and the open-circuit faults of diodes and MOSFETs are mainly analyzed experimentally. The simulation results show that the expected effect has been achieved in the fault diagnosis of the on-board charger circuit, and the established simple BP neural network model can be applied in practice. Some feasible solutions are provided for improving the charging safety of electric vehicles.

Intelligent Evaluation of Product Form Design Based on Deep Neural Network Model

In order to evaluate the product morphological design, a method of applying BP neural network to evaluate the product morphological design is proposed based on the analysis of the principle of artificial neural network. In such method, the advantages of the BP neural network with self-learning, self-organization, self-adaptation, and nonlinear dynamic processing are applied to effectively evaluate the product morphological design. Specifically, 13 product morphological design solutions of the automotive evaluation data set are selected as samples, 15 out of 18 solutions are used to train the evaluation system, and the remaining 3 solutions are used to validate the trained system. The validation results show that the relative errors between the simulated and actual values are 3.6%, −1.7%, and 2.8%, respectively. Such results also show high accuracy and simultaneously can reflect the effectiveness of our proposed system for evaluating the design solutions.

Application of BP Neural Networks in Garment Pattern Design System.

With the intensification of global market competition and the continuous development of the information technology, competition in the apparel market has become increasingly fierce. The key to whether China's garment industry can maintain its advantage in the international market competition in the future lies in whether it can promote and realize the informatization of the garment industry or not. After all, under the context of increasingly developed information technologies and growing competition in the garment market, mass customization of garments has become a future trend in the garment industry. As custom-made clothing is more in line with consumers' individual needs in terms of style, fabric, and size, the focus of development for clothing companies is increasingly on the grasp of the fit of clothing. However, with China's large population and the wide variety of body types, traditional hand-made garments are time-consuming and cannot meet the differentiated needs of consumers in the modern market. The design of garment samples is an important part of the industrial production of garments and is highly dependent on the skills and experience of the operators. In other words, the level of technical expertise can determine the quality and shape of a garment product to a certain extent. As a result, in order to further improve the efficiency and quality of garment sample design and to reduce the dependence on operator skills and experience, this study proposes an intelligent garment paper sample design system based on BP neural networks. The system mainly utilizes the self-learning, self-organizing, and adaptive as well as nonlinear mapping functions of artificial neural networks to design clothing samples autonomously, thus improving the design efficiency. In the era of rapid development of information technology and artificial intelligence technology, the development of intelligent garment pattern design systems with independent intellectual property rights is of great significance in promoting the prosperity of the garment industry.

Research on the Application of Adaptive Matching Tracking Algorithm Fused with Neural Network in the Development of E-Government

With the development of information technology and the deepening of government system reform, e-government has become one of the key points of government construction. However, both in developing countries and developed countries, there is a common phenomenon that the success rate of e-government projects is not high. Although various e-government evaluation index systems have been proposed in both theoretical research and application aspects, which provide strong support for the development of e-government, these index systems still have limitations. This paper comprehensively applies BP neural network and adaptive matching tracking, and constructs an e-government performance evaluation model based on adaptive matching neural network. We conduct simulation experiments on the constructed model to verify the accuracy and rationality of the model evaluation. It is feasible to introduce the Balanced Scorecard to construct the E-government performance evaluation index system. The constructed index system has strong objectivity, operability, comprehensiveness, guidance and sustainability, and can be applied to the practice of e-government performance evaluation. It is feasible to use the adaptive matching tracking neural network model to evaluate the performance of e-government. The model has the ability to self-learn the evaluation samples, and can grasp the mapping relationship between the evaluation indicators and the evaluation results, so as to imitate the evaluation of experts, and overcome the evaluation randomness and subjective uncertainty that are difficult to get rid of in traditional manual evaluation. In order to better evaluate the operation status of the e-government system and its adaptability to future needs, and find out its existing problems, this paper fully considers the interaction process between the government and enterprises, and establishes a corresponding responsive government. The application of adaptive matching tracking neural network model for e-government performance evaluation has better optimization results than the original BP neural network model. The specific performance is that the convergence speed is fast, the training will not fall into a local minimum, and the error of the evaluation results is small.

VHCF evaluation with BP neural network for centrifugal impeller material affected by internal inclusion and GBF region

The application of back propagation neural network (BP neural network) in very-high cycle fatigue life evaluation of centrifugal impeller to explore the effect of internal inclusion and granular bright facet (GBF) region on the fatigue life is a key and potential issue. Numerical simulation analysis of centrifugal impeller is carried out in this study to clear the mechanical state of centrifugal impeller in operation condition. Then, the designed very-high cycle fatigue test is conducted out; the test data and fracture morphology are analyzed to reveal the effect of internal inclusion and GBF region on the fatigue failure and life distribution. Then, with the comprehensive application of BP neural network, the fatigue life with different input parameters are predicted. In the case of different input parameters, the prediction changed and the very-high cycle fatigue life with the consideration of both internal inclusion and GBF region is very satisfactory. Study on neural network fatigue life prediction approach of centrifugal impeller in VHCF affected by internal inclusion and GBF region is novel for the further fatigue study in theoretical research and engineering practice for mechanical component and engineering metallic material.

Application of BP Neural Network in Efficiency Prediction of Oilfield Mechanized Mining System

Application of BP Neural Network in Efficiency Prediction of Oilfield Mechanized Mining System

Neural network-based optimization model for sprint data collection

Sprint data has the characteristics of quality and continuity, but due to the limitations of optimization algorithm, the existing sprint data acquisition optimization model has the problem of low optimization performance parameters. Therefore, a data acquisition control optimization model based on neural network is proposed. This paper analyzes the advantages and disadvantages of neural network algorithm, combined with the sprint data collection optimization requirements, introduces BP neural network algorithm, based on this, uses multiple sensors, based on baud interval balance control to collect sprint data, applies BP neural network algorithm to compress, integrate and classify sprint data, realizes the sprint data collection and optimization. The experimental results show that the optimization performance parameters of the model are large, which fully shows that the model has good data acquisition optimization performance.

Study on the application of BP neural network optimized based on various optimization algorithms in storm surge prediction

Storm surge prediction is of great importance to disaster prevention and mitigation. In this study, four optimization algorithms including genetic algorithm (GA), particle swarm optimization (PSO), beetle antenna search (BAS), and beetle swarm optimization (BSO) are used to optimize the back propagation neural network (BPNN), and four optimized BPNNs for storm surge prediction are proposed and applied to Yulin station and Xiuying station at Hainan, China. The optimal model parameter combination is determined by trail-and-error method for the best prediction performance. Comparisons with the single BPNN indicate that storm surge can be efficiently predicted using the optimized BPNNs. BPNN optimized by BSO has the minimum prediction error, and BPNN optimized by BAS has the minimum time cost to reduce unit prediction error.

Application of BP Neural Network Based on the Genetic Algorithm in Secondary Modeling of Air Quality Forecast

It is one of the effective methods improving the ambient air quality to establish air quality forecast models to know the possible air pollution process in advance and take corresponding control measures. The secondary modeling of air quality forecast was explored. By determining the definition and calculation method of air quality index (AQI) and primary pollutants, and according to the degree of influence on pollutant concentration, five meteorological conditions, namely temperature, humidity, air pressure, wind speed and wind direction, were classified by a k-means clustering algorithm in line with AQI. According to the measured data and primary forecast data of three monitoring points (A, B and C), a secondary forecast model was developed using BP neural network based on the genetic algorithm. The results show that the weather favorable for pollutant diffusion had the conditions of high wind speed and high air pressure, while the weather unfavorable for pollutant diffusion had the conditions of low wind speed and low air pressure. The secondary forecast model has good prediction accuracy, and can simultaneously predict the concentrations of multiple monitoring points, many days in the future and various pollutants, thus better forecasting the air quality. The built secondary forecast model can improve the accuracy of air quality forecast, and has high economic and ecological value.

Application of BP Neural Network in Classification and Prediction of Blended Learning Achievements

Analyzing and predicting the learning behavior data of students in blended teaching can provide reference basis for teaching. Aiming at weak generalization ability of existing algorithm models in performance prediction, a BP neural network is introduced to classify and predict the grades of students in the blended teaching. L2 regularization term is added to construct the BP neural network model in order to reduce the risk of overfitting. Combined with Pearson coefficient, effective feature data are selected as the validation dataset of the model by mining the data of Chao-Xing platform. The performance of common machine learning algorithms and the BP neural network are compared on the dataset. Experiments show that BP neural network model has stronger generalizability than common machine learning models. The BP neural network with L2 regularization has better fitting ability than the original BP neural network model. It achieves better performance with improved accuracy.