BP Model Research Articles

Study on thermal error modeling of high-speed motorized spindle considering bearing inner ring temperature

In addressing the challenge of unattainable temperature data at critical heat source points in bearings, this study solves for the temperatures of the bearing inner ring and rollers by merging thermal resistance network calculations with experimental data, referencing finite element simulation outcomes. The impact of temperature data from various positions within bearings on model accuracy is assessed using a BP prediction model. Subsequently, the training dataset of the thermal error model is optimized to enhance its predictive performance. To further boost the predictive capabilities of the model, a whale optimization algorithm is employed to optimize the BP neural network, which is then compared against both the conventional BP model and the GPR model. The findings reveal that a model optimized with the input of inner ring temperatures and the WOA algorithm achieves an accuracy rate of 97.748 %, outperforming both the BP model and the GPR model. This study provides a new idea for the field of thermal error modeling of motorized spindle.

Experimental analysis and model prediction of elbow pipe's erosion in water-cooled radiator

The radiator with heat transfer capability is able to guarantee the stable operation of hydro generator set, while the long-term and continuous scouring on radiator pipes by cooling medium will lead to thinning or even perforation of pipe wall, which triggers wall failure. This paper analyzes and predicts the failure mechanism of radiator’s pipe wall, and investigates the effects of water flow velocity, sand content and sand particle size on erosion damage of radiator pipe by establishing a test bench for pipe erosion. The results show that the increase of above parameters will lead to the increasing erosion rate, especially when the sand content is 1%, the velocity is 8 m/s and the sand particle size is 0.85 mm, the erosion damage will be particularly serious. Based on experimental data, BP and LSSVM models are employed to predict the pipe wall failure, and PSO algorithm is used to optimize the two models. The optimized PSO-BP has the highest accuracy with the mean absolute error (MAE) of 0.2070 and the mean absolute percentage error (MAPE) of 4.702%. The findings provide a reference for wall failure analysis of radiator, which is of great significance for unit's safe operation.

MAU-net: A multibranch attention U-net for full-waveform inversion

Data-driven velocity inversion has emerged as a prominent and challenging problem in seismic exploration. The complexity of the inversion problem and the limited data set make it difficult to ensure the stability and generalization of neural networks. To address these concerns, a novel approach called multibranch attention U-net (MAU-net) is proposed for velocity inversion. The key distinction of MAU-net from previous data-driven approaches lies in its ability to not only learn information from the data domain but also incorporate prior model domain information. MAU-net consists of two branches: one branch uses seismic records as input to effectively learn the mapping relationship between the data and model domains, whereas the other branch uses a prior geologic model as input to extract features from the model domain, thereby guiding MAU-net’s learning process. In addition, three major improvements are made to the model to enhance MAU-net’s utilization of seismic data and handle redundant information. The effectiveness of the improvements is verified through ablation experiments. The performance of MAU-net is demonstrated with the Marmousi model and the 2004 BP model, and it also can be combined with full-waveform inversion to further improve the quality of the inversion result. MAU-net exhibits robust performance on field data through the use of transfer learning techniques, further confirming its reliability and applicability.

The Study of Various Regression Models Establishment to Identify Farmland Soil Moisture Content at Different Depths Using Unmanned Aerial Vehicle Multispectral Data: A Case in North China Plain

Soil moisture content is one of the most important soil indices for agriculture production. With the increasing food requirement and limited irrigation water sources, it is of great significance to accurately and quickly measure the soil moisture content for precision irrigation, especially in deficient agricultural areas, such as North China Plain. To achieve this goal, more attention was paid to the application of unmanned aerial vehicle multispectral reflectance technology. However, it was urgent to enhance the regression models between spectral data and soil realistic moisture content, and there were limited studies about the regression research on deep soil layers. Thus, the farmland of winter wheat–summer maize double cropping at Yongnian District, Hebei, North China, was selected as the study area. A six-band multispectral camera mounted on a low-altitude unmanned aerial vehicle (UAV) was used to obtain the field spectral reflectance with bands from 470~810 nm, and meanwhile, soil moisture content at different depths (10, 20, 30, 40, 50, and 60 cm) was measured after maize sowing. Unary linear regression (ULR), multivariate linear regression (MLR), ridge regression (RR), and an artificial neural network (ANN) were employed to establish regression models. The results demonstrated that the sensitive bands of spectral reflectance were 690 nm, 470 nm, and 810 nm. Those models all established significant regression at the depths of 0–20 cm and 40–60 cm, particularly at 10, 50, and 60 cm soil layers. However, for a depth of 20–40 cm, the prediction accuracy was generally lower. Among MLR, RR, and BP models, the MLR exhibited the highest identification accuracy, which was most recommended for the application. The findings of this study provide technical guidance and effective regression for the multispectral reflectance on the farmland of North China Plain, especially for deep soil layer moisture prediction.

The measurement error prediction method of voltage transformer based on TCN-RBF

Accurate prediction of voltage transformer error is an important guarantee for the stable and economic operation of the power grid. This paper presents a measurement error prediction method for voltage transformers based on time convolution and radial basis neural networks (TCN-RBF). Firstly, the ensemble empirical mode decomposition is used to decompose the original error sequence into multiple components. Secondly, a TCN prediction model was established based on each component and historical error sequence to achieve error prediction. To further improve the prediction progress, the RBF model was used to further fit the predicted results with the environmental parameter data and confirm the final predicted value. The operation error of the transformer in a substation is tested. The test results show that compared with LSTM, SVM, BP, and other prediction models, this method has better performance in error prediction.

Turbofan Engine Health Prediction Model Based on ESO-BP Neural Network

Predicting the health index of turbofan engines is critical in reducing downtime and ensuring aircraft safety. This study introduces the elite snake optimizer-back propagation (ESO-BP) model to address the challenges of low accuracy and poor stability in predicting the health index of turbofan engines through neural networks. Firstly, the snake optimizer (SO) was improved into the elite snake optimizer (ESO) through an elite-guided strategy and a reverse learning mechanism. The performance improvement was validated using benchmark functions. Additionally, feature importance was introduced as a feature selection method. Finally, the optimization results of the ESO were employed to set the initial weights and biases of the BP neural network, preventing convergence to local optima. The prediction performance of the ESO-BP model was validated using the C-MAPSS datasets. The ESO-BP model was compared with the CNN, RNN, LSTM, baseline BP, and unimproved SO-BP models. The results demonstrated that the ESO-BP model has a superior accuracy with an impressive R-squared (R2) value of 0.931 and a root mean square error (RMSE) of 0.060 on the FD001 sub-dataset. Furthermore, the ESO-BP model exhibited lower standard deviations of evaluation metrics on 100 trials. According to the study, ESO-BP demonstrated a greater prediction accuracy and stability when compared to commonly used models such as CNN, RNN, LSTM, and BP.

Implementation of an Artificial Neural Network Based on the Backpropagation Algorithm in Forecasting the Closing Price of the Jakarta Composite Index (IHSG)

Investing is highly common in Indonesia. Continuous investment activities carried out by the community will increase economic activity and employment opportunities, increase national income, and increase the level of prosperity of the community. In carrying out share buying and selling transactions, there is a means for companies to obtain funds from official financiers or investors, which is called the capital market. One of the indices issued by the IDX is the Jakarta Composite Index (IHSG). Statistics can be used to help investors, the government, or related institutions to predict the value of the IHSG. One method that can be used to predict data is an Artificial Neural Network (ANN). Backpropagation method is a multi-layer ANN method that works in a supervised learning. The idea of the Backpropagation algorithm is that the input of the neural network is evaluated against the desired output results of the research showed that the BP (4,6,1) model produced an RMSE value of 28,24024 and a MAPE value of 0.00342%. Based on the results of this research, an Artificial Neural Network model based on the Backpropagation Algorithm can be applied to predict the IHSG Closing Price value.

Consistency of the inverse scattering imaging condition, the energy norm imaging condition and the impedance kernel in acoustic and elastic reverse-time migration

Abstract In this work, we draw connections between the imaging conditions using the impedance kernel, the inverse scattering imaging condition, and the energy norm imaging condition in acoustic and elastic reverse-time migration (RTM). Traditional RTM often introduces large low-wavenumber artifacts that degrade image quality in intricate geological structures with large velocity variations. In practice, the Laplacian filter is commonly used to remove these low-wavenumber artifacts, but it changes the image wavenumber spectrum. The advanced imaging conditions of the inverse scattering, the energy norm, and the impedance kernel can effectively remove the low-wavenumber artifacts while not changing the wavenumber spectrum. This study aims to build a connection between these three types of imaging conditions by conducting detailed analysis in the wavenumber domain for acoustic and elastic RTMs. We find that they are exactly the same except for the varying weights of the source-receiver wavefield cross-correlation. All three imaging conditions can generate clear RTM images that are not affected by low-wavenumber artifacts. Numerical examples for a simple model, Sigsbee 2a, and BP models verify the consistency of these three imaging conditions and show their advantage over conventional simple zero-lag cross-correlation imaging conditions. This is important for improving the quality and reliability of seismic imaging technology.

Prediction of Energy Consumption in a Coal-Fired Boiler Based on MIV-ISAO-LSSVM

Aiming at the problem that the energy consumption of the boiler system varies greatly under the flexible peaking requirements of coal-fired units, an energy consumption prediction model for the boiler system is established based on a Least-Squares Support Vector Machine (LSSVM). First, the Mean Impact Value (MIV) algorithm is used to simplify the input characteristics of the model and determine the key operating parameters that affect energy consumption. Secondly, the Snow Ablation Optimizer (SAO) with tent map, adaptive t-distribution, and the opposites learning mechanism is introduced to determine the parameters in the prediction model. On this basis, based on the operation data of an ultra-supercritical coal-fired unit in Xinjiang, China, the boiler energy consumption dataset under variable load is established based on the theory of fuel specific consumption. The proposed prediction model is used to predict and analyze the boiler energy consumption, and a comparison is made with other common prediction methods. The results show that compared with the LSSVM, BP, and ELM prediction models, the average Relative Root Mean Squared Errors (aRRMSE) of the LSSVM model using ISAO are reduced by 2.13%, 18.12%, and 40.3%, respectively. The prediction model established in this paper has good accuracy. It can predict the energy consumption distribution of the boiler system of the ultra-supercritical coal-fired unit under variable load more accurately.

Short-Term Prediction of the Intermediate Point Temperature of a Supercritical Unit Based on the EEMD–LSTM Method

The quality of the intermediate point temperature control of a supercritical unit is directly related to the quality of the coal–water ratio and main steam temperature control of the supercritical unit, which is also related to the economy and safety of the unit. In order to improve the accuracy of short-term predictions of the intermediate point temperature, a short-term prediction model of the intermediate point temperature based on the EEMD (Ensemble Empirical Mode Decomposition)-LSTM (Long Short-Term Memory) model is proposed. This model uses the data of a 600 MW thermal power station in 2022 as a sample. The EEMD method is used to decompose the historical data into IMF components and residual components, and the correlation between each component and the original data is calculated. The relevant components are sent to the LSTM neural network, and all the sub-components are superimposed to obtain the final intermediate point temperature prediction results. At the same time, the BP and LSTM models are built to compare the errors with the proposed model. The results show that the single model will produce large errors when predicting the factors of large data fluctuations. The EEMD–LSTM coupling model can fully extract the detailed features and the prediction effect is obvious. The prediction accuracy of the EEMD–LSTM prediction model built in this paper is significantly better than that of the other two models. It has certain application value in the research field of intermediate point temperature prediction and can meet the requirements of short-term predictions of the intermediate point temperature.

Carbon trading price forecasting based on parameter optimization VMD and deep network CNN–LSTM model

To meet carbon peak and neutrality targets, accurate carbon trading price forecasting is very important for enterprises making emission reduction decisions. By fusing convolutional neural network (CNN) and long short-term memory network (LSTM), the CNN–LSTM model is constructed. After variational mode decomposition (VMD), several intrinsic mode functions (IMFs) components are obtained and input into the CNN–LSTM model, thus constructing the combined sooty tern optimization algorithm (STOA)–VMD–CNN–LSTM forecasting model. To test this model, the carbon trading prices of the carbon emission trading markets of Hubei, Guangdong and Shenzhen were forecast. The prediction performance of the STOA–VMD–CNN–LSTM model is compared with ARIMA, BP, CNN and LSTM benchmark models and models combining different decomposition technologies. The international carbon trading price (EUR and CER) is used for prediction. Compared with other methods, the developed model makes fewer errors and achieves superior performance. Several important implications are provided for investors and risk managers involved in carbon financial products.

Using Machine Learning Models to Forecast the Conversion Coefficient between Electricity Consumption and Water Pumped for Irrigation Wells in Baicheng City, China

Forecasting the electricity-to-water conversion coefficient (EWCC) can help manage and plan irrigation water in arid and semiarid areas. However, the EWCC is influenced by several factors, making it difficult to develop an analytical model for validation or prediction. Therefore, this study selected 206 typical irrigation wells in Baicheng City to conduct EWCC tests in a field investigation to gather information regarding the results and related influencing factors. Subsequently, machine learning models (multiple linear regression model, support vector model, and backpropagation neural network) were trained, validated, and tested, and their precisions were evaluated and compared. The backpropagation neural network model was the most accurate, followed by the support vector and multiple linear regression models. The backpropagation neural network model results were consistent with those of the field survey, and this model was thus used to forecast the EWCC for all the townships in Baicheng City. The forecasting models revealed that most towns had an EWCC from 3 to 7 m3/kW·h, with an EWCC greater than 7 observed in the Tao’er River Fan and Yueliangpao District. The BP models developed in this study proved to be dependable and applicable for forecasting the EWCC in this area.

Exploring the Carbon Footprint of Energy Consumption in Shanxi Province Based on the Tapio Decoupling and Grey BP Neural Network Model

This paper focuses on analyzing the carbon footprint of energy consumption in Shanxi Province, China. It utilizes data from sources such as the 'China Energy Statistical Yearbook' (2001-2022), the China Carbon Accounting Database, and the 'Shanxi Statistical Yearbook' for an exploratory analysis of energy consumption and the province's economy. The study calculates the carbon footprint of energy consumption in Shanxi Province and employs the LMDI model to identify influencing factors. Additionally, it uses the Tapio decoupling model to assess the relationship between these factors and economic development. To forecast the carbon footprint from 2022 to 2026, a grey BP neural network prediction model is applied, demonstrating improved accuracy compared to BP and grey models, with MAE and RMSE values of 2.44 and 10.91.

A Method for Predicting Ground Pressure in Meihuajing Coal Mine Based on Improved BP Neural Network by Immune Algorithm-Particle Swarm Optimization

Based on the background of dynamic mining pressure monitoring and pressure prediction research on the No. 232205 working face of the Meihuajing coal mine, this study systematically investigates the predictive model of mining pressure manifestation on the working face of the Meihuajing coal mine by integrating methods such as engineering investigation, theoretical analysis, and mathematical modeling. A mining pressure manifestation prediction method based on IA-PSO-BP is proposed. The IA-PSO optimization algorithm is applied to optimize the hyperparameters of the BP neural network, and the working face mining pressure prediction model based on IA-PSO-BP is established. The mean absolute error (MAE), mean square error (MSE), and coefficient of determination (R2) are selected as evaluation indicators to compare the prediction performance of the BP model, PSO-BP model, and IA-PSO-BP model. The experimental results of the model show that the convergence speed of the IA-PSO-BP model is about eight times faster than that of the BP model and two times faster than that of the PSO-BP model. Compared with the BP and PSO-BP models, the IA-PSO-BP model has the smallest MAE and MSE and the largest R2 on the three different data sets of the test set, indicating significantly improved prediction accuracy. The predicted results conform to the periodic variation pattern of mining pressure data and are consistent with the actual situation in the coal mine.

Information Flow and Knowledge Management Strategies in the Digital Transformation of Party History and Party Building Education

Abstract In this paper, a relatively complete digital education management information system is established for the digital basic education resources involved in the field of basic education of Marxist theory to realize the digital transformation of party history and party-building education. Faced with a large amount of student data, the improved new BP model is utilized for integration, cataloging, and prediction. The data elements describing the resources are used as semantic tags to generate XLM documents in a standardized format, which enables the system to exchange data with other resource systems easily. It can be observed that the BP model accurately predicts and analyzes the shared learning behaviors of certain students with a prediction accuracy of 98% upon analysis. The educational management information system proposed in this paper can effectively identify potential risk-takers and categorize learning groups. The indicator of resource-sharing learning behavior has a significant effect on the learning effect of Marxist theory (P<0.05).

F10.7 Daily Forecast Using LSTM Combined With VMD Method

AbstractThe F10.7 solar radiation flux is a well‐known parameter that is closely linked to solar activity, serving as a key index for measuring the level of solar activity. In this study, the Variational Mode Decomposition (VMD) and Long Short‐term Memory (LSTM) network are combined to construct a VMD‐LSTM model for predicting F10.7 values. The F10.7 sequence is decomposed into several intrinsic mode functions (IMF) by VMD, then the LSTM neural network is utilized to forecast each IMF. All IMF prediction results are aggregated to obtain the final F10.7 value. The data sets from 1957 to 2008 are used for training and the data sets from 2009 to 2019 are used for testing. The results show that the VMD‐LSTM model achieves an annual average root mean square error of only 4.47 sfu and an annual average correlation coefficient (R) of 0.99 during solar cycle 24, which is significantly better than the accuracy of the LSTM model (W. Zhang et al., 2022, https://doi.org/10.3390/universe8010030), the AR model (Du, 2020, https://doi.org/10.1007/s11207-020-01689-x), and the BP model (Xiao et al., 2017, https://doi.org/10.11728/cjss2017.01.001). The VMD‐LSTM model exhibits strong predictive capability for the F10.7 index during solar cycle 24.

Analysis of new energy vehicle battery temperature prediction by combining BP neural network

Abstract With the rapid development of the new energy industry, the safety research of battery technology has become a key topic. This paper focuses on the temperature prediction of new energy vehicle batteries, aiming to improve the safety and efficiency of batteries. Based on the new energy vehicle battery management system, the article constructs a new battery temperature prediction model, SOA-BP neural network, using BP neural network optimized by SOA algorithm. This model can accurately predict the battery temperature, and the effectiveness of its temperature control is verified through experiments. The results show that the SOA-BP neural network model outperforms the traditional BP, CNN, and RNN models in temperature prediction. Regarding evaluation indexes, the model’s root mean square error (RMSE), mean absolute error (MAE), and R2_Score are 0.953, 0.909, and 0.837, respectively. It is worth noting that the model can effectively regulate and control the battery temperatures at different temperatures, ensuring that the maximum temperature difference of each battery is maintained within 5°C. The model can also be used to predict the temperature of the batteries in different temperatures. This battery temperature prediction model not only provides an effective means for predicting and controlling the battery temperature of new energy vehicles, but also provides an essential reference for improving the vehicle’s performance and developing energy management strategies. This study offers a new solution for the safety and efficiency of new energy vehicle batteries.

RETRACTION: Realization of Intelligent Scoring System of Taekwondo Protective Gear under the Application of Neural Network BP Model.

[This retracts the article DOI: 10.1155/2022/5902983.].

Quantitative prediction model and prewarning system of water yield capacity (WYC) from coal seam roof based on deep learning and joint advanced detection

As one of the most abundant fossil fuels in the world, the safe and efficient mining of coal resources has greatly affects all fields of human production and life. Accurate prediction of the water yield capacity (WYC) of coal seam roofs and advanced detection of water abundance is of great significance to preventing roof water disasters in coal mines. In the process of coal mining, complex roof water inrush mechanism, and changeable factors are faced. Moreover, the uncertainty of the interconnection between various factors increases the difficulty of roof water disaster prevention. To improve the accuracy and reliability of WYC prediction, this study analyzed the main control factors of coal seam roof water inrush and built a WYC quantitative prediction model based on the SAA-CNN algorithm. 47 samples (40 training sets and 7 test sets) were selected to train and learn the model, and the WYC of the 3rd coal mining roof in Jiangzhuang Coal Mine (JZCM) was predicted. To verify the reliability of the model, BP, RF, SVM, CNN, and SAA-CNN models were used for training and error comparison. Finally, the 1001 working face of JZCM was selected to use a variety of geophysical methods and drilling combined for advanced detection and construct the dynamic monitoring and prewarning system of coal seam roof water inrush risk. The results show that the accuracy of the WYC prediction model is SAA-CNN＞RF＞CNN＞SVM＞BP. The average absolute error of the SAA-CNN model is 0.87 m3/h, and the average relative error is 3.56 %. In this study, the quantitative and accurate prediction of WYC of coal seam roofs is realized. Combined with the advanced detection method of “electric-magnetic-drilling”, the prewarning system of water inrush risk of mining is constructed, which provides a new method for the prevention of water disasters in coal seam roofs.

Economic loss assessment of typhoon-induced storm surge disasters in the South China Sea based on GSA-BP model

In the context of global climate warming and rising sea levels, the frequency of tropical cyclones in the South China Sea region has shown a significant upward trend in recent years. Consequently, the coastal areas of the South China Sea are increasingly vulnerable to storm surge disasters induced by typhoon, posing severe challenges to disaster prevention and mitigation in affected cities. Therefore, establishing a multi-indicator assessment system for typhoon storm surges is crucial to provide scientific references for effective defense measures against disasters in the region. This study examines 25 sets of typhoon storm surge data from the South China Sea spanning the years 1989–2020. A comprehensive assessment system was constructed to evaluate the damages caused by storm surges by incorporating the maximum wind speed of typhoons. To reduce redundancy among multiple indicators in the assessment system and enhance the stability and operational efficiency of the storm surge-induced disaster loss model, the entropy method and bootstrap toolbox were employed to process post-disaster data. Furthermore, the genetic simulated annealing algorithm was utilized to optimize a backpropagation neural network intelligent model (GSA-BP), enabling pre-assessment of the risks associated with storm surge disasters induced by typhoon and related economic losses. The results indicate that the GSA-BP model outperforms the genetic algorithm optimized BP model (GA-BP) and the simulated annealing algorithm-optimized BP model (SA-BP) in terms of predicting direct economic losses caused by storm surges. The GSA-BP model exhibits higher prediction accuracy, shorter computation time, and faster convergence speed. It offers a new approach to predicting storm surge losses in coastal cities along the South China Sea.