Back Propagation Neural Network Algorithm Research Articles

IMPROVING THE EVALUATION AND PREDICTION OF PREVENTION AND TREATMENT EFFICIENCY DURING PUBLIC HEALTH EMERGENCIES BY USING THE SBM-BPNN ALGORITHM

Crisis response is the key to reducing the impact and damage of public health emergencies. Previous studies only focused on specific measures for emergency response, but the effectiveness of these measures has seldom been discussed. How about the effective of the crisis response on public health emergencies? What will happen to the effectiveness of crisis response after the transformation of public health emergency measures? To address these questions, a novel research framework was constructed by combining slacks-based measure (SBM) and back propagation neural network (BPNN) algorithms. At Stage I, an efficiency evaluation was conducted on SBM with undesirable outputs to evaluate the prevention and treatment efficiency during public health emergencies, and stochastic frontier analysis was performed to mitigate the influence of environmental factors and statistical noise. At Stage II, the adjusted efficiency was predicted with the BPNN model. The decision-making units were effectively improved by incorporating slack variables, thus enabling the prediction and optimization of optimal cases given the epidemic resources. An empirical analysis of the response of 43 G20 member countries to the COVID-19 pandemic showed that the novel framework could evaluate prevention and treatment efficiency across regions and predict the efficiency and optimal case outputs following shifts in epidemic preventive measures. After evaluation, the mean squared error of the BPNN efficiency prediction model was only 0.0014, whereas that of the BPNN optimal output prediction model was 0.126. Therefore, this novel framework is suitable for evaluating and predicting the effectiveness of crisis response on public health emergencies, which provides necessary assistance for crisis decision-making by the government and emergency management organizations., Keywords: Public health emergencies, COVID-19 pandemic, Crisis response efficiency, Evaluation, Prediction.

A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks

Mega-constellation network traffic forecasting provides key information for routing and resource allocation, which is of great significance to the performance of satellite networks. However, due to the self-similarity and long-range dependence (LRD) of mega-constellation network traffic, traditional linear/non-linear forecasting models cannot achieve sufficient forecasting accuracy. In order to resolve this problem, a mega-constellation network traffic forecasting model based on EMD (empirical mode decomposition)-ARIMA (autoregressive integrated moving average) and IGWO (improved grey wolf optimizer) optimized BPNN (back-propagation neural network) is proposed in this paper, which makes comprehensive utilization of linear model ARIMA, non-linear model BPNN and optimization algorithm IGWO. With the enhancement of the global optimization capability of a BPNN, the proposed hybrid model can fully realize the potential of mining linear and non-linear laws of mega-constellation network traffic, hence improving the forecasting accuracy. This paper utilizes an ON/OFF model to generate historical self-similar traffic to forecast. RMSE (root mean square error), MAE (mean absolute error), R-square and MAPE (mean absolute percentage error) are adopted as evaluation indexes for the forecasting effect. Comprehensive experimental results show that the proposed method outperforms traditional constellation network traffic forecasting schemes, with several improvements in forecasting accuracy and efficiency.

Relational model of compositions of enamel coatings and sintering temperature based on back propagation neural network algorithm

Declining the sintering temperature of enamel coatings is traditionally by reducing the content of network-forming agents (such as SiO2) and increasing the content of flux (for example, alkali metal oxides), which will lower the density of the [-Si-O-] network structure, and adversely affect the corrosion resistance of enamel coatings. To ensure a low sintering temperature of enamel coatings as much as possible under a high SiO2 content, it is essential to develop novel enamel coating formulas. However, abundant compositions of enamel coatings cause enormous verification workloads. To address this issue, this paper establishes a relational model of the compositions of enamel coatings and the sintering temperature using the back propagation (BP) neural network algorithm and proposes a formula that meets the requirement of lower the sintering temperature under a high SiO2 content. When the SiO2 content is 55 %, the sintering temperature can be diminished to around 400 °C. Furthermore, the influences of common enamel components on the sintering temperature are identified in this paper using the sensitivity algorithm. The obtained general law of formulas is that the total weight value of the formula at low sintering temperatures should be around −0.3. This paper can provide valuable theoretical guidance for subsequent research and expand the application range of enamel coatings.

Prediction modeling of cigarette ventilation rate based on genetic algorithm backpropagation (GABP) neural network

AbstractThe ventilation rate of cigarettes is an important indicator that affects the internal quality of cigarettes. When producing cigarettes, the unit may experience unstable ventilation rates, which can lead to a decrease in cigarette quality and pose certain risks to smokers. By establishing the ventilation rate prediction model, guide the design of unit parameters in advance, to achieve the goal of stabilizing unit ventilation rate, improve the stability of cigarette ventilation rate, and enhance the quality of cigarettes. This paper used multiple linear regression networks (MLR), backpropagation neural networks (BPNN), and genetic algorithm-optimized backpropagation (GABP) to construct a model for the prediction of cigarette ventilation rate. The model results indicated that the total ventilation rate was significantly positively correlated with weight (P < 0.01), circumference, hardness, filter air permeability, and open resistance. The results showed that the MLR models' (RMSE = 0.652, R2 = 0.841) and the BPNN models’ (RMSE = 0.640, R2 = 0.847) prediction ability were limited. Optimization by genetic algorithm, GABP models were generated and exhibited a little better prediction performance (RMSE = 0.606, R2 = 0.873). The results indicated that the GABP model has the highest accuracy in the prediction of predicting ventilation rate and can accurately predict cigarette ventilation rate. This method can provide theoretical guidance and technical support for the stability study of the ventilation rate of the unit, improve the design and manufacturing capabilities and product quality of short cigarette products, and help to improve the quality of cigarettes.

Optimal Operation Model of Ecological Flow of Hydropower Station Based on Genetic Algorithm and Neural Network

In view of the contradiction between the ecological flow regulation of the reservoir River and the power generation flow regulation of the reservoir with the largest power generation, this paper takes the maximum power generation and the maximum average ecological protection degree as the objective function, and uses the genetic algorithm back propagation neural networks (GA-BP) algorithm to establish the reservoir flow regulation model under the constraints of water balance, reservoir capacity, unit output and unit overflow, The optimal solution of the dispatching scheme is carried out, and the Shulehe hydropower station is taken as an example to verify the optimal scheme. The results show that the optimized dispatching model can ensure the demand of ecological flow, increase 32680 kW·h power generation and 23.85% power generation benefit, so as to provide scientific reference for the rational planning, dispatching and utilization of reservoir water resources.

An Automatic Frequency Control System for Transmission Machinery Based on Back-Propagation Neural Network Algorithm in the Internet of Things Environment

Abstract In order to reduce the error of automatic frequency control of transmission machinery under different disturbances, an automatic frequency control system of transmission machinery based on back-propagation (BP) neural network algorithm is designed. The gradient descent method and Newton method are combined to optimize the BP neural network. Aiming at the periodicity and trend of the actual mechanical vibration frequency time series, the first order backward difference is processed by the difference method, and the autoregressive sequence is derived to obtain the control model. The experimental results show that the system can realize the automatic control of transmission mechanical frequency with small error under different types of interference, and the control effect is ideal.

A decision-aid system for subway microenvironment health risk intervention based on backpropagation neural network and permutation feature importance method

Subway has become an important commute mode in daily life. However, subway microenvironment poses a threat to people's health and there is growing concern about its health risks. In this paper, a decision-aid system for subway microenvironment health risk intervention was proposed based on the combination of backpropagation neural network algorithm and permutation feature importance method. The non-linear relationship between indicators and the risks of subway microenvironment was established using the neural network. On this basis, the implementation of stepwise intervention, neural network prediction, and the generation of strategies was achieved utilizing MATLAB programming. Moreover, a subway station in Nanjing was taken as an example to illustrate the implementation and verify the effectiveness of the system. With the help of the system, health risks in the subway microenvironment can be systematically analyzed, efficiently optimized, and gradually solved. Our research contributes to breaking the deadlock in subway microenvironment intervention, which has long been confined to predicting and summarizing strategies. It provides more objective and effective intervention strategies for the health risk management of subway microenvironments.

Application of Deep Belief Network in Weather Modeling: PM2.5 Concentration in Thailand

In Thailand, the number of particles matter with diameter of less than 2.5 microns or PM2.5 concentration exceed the standard in many areas, especially in Chiang Mai. This affects the image of the country in terms of economy, health, and environment. The objective of this research is to study the structure of model for PM2.5 concentration by using a Deep Belief Network (DBN) with the daily data set of PM2.5 concentration from the air quality monitoring station at Yupparaj Wittayalai School, Chiang Mai. The data was analyzed through an unsupervised path using the Minimizing Contrastive Divergence (MCD) algorithm, followed by a supervised path using Back-Propagation Neural Network (BPNN) algorithm to estimate the parameters of DBN. The result shows that the optimal DBN structure has 5 input nodes and 20 hidden neurons in the first hidden layer. This model has an 88.4 percent accuracy in forecasting PM2.5 concentration. In addition, this model can be applied for other weather forecasting such as rainfall or water level in a basin.

Bed density prediction of gas–solid separation fluidized bed based on genetic algorithm-back propagation neural network

The fluctuation of bed density in the process of mineral separation in gas–solid fluidized bed is particularly critical, and accurate prediction of bed density will play a crucial role in industrial production. In this paper, the axial and radial distribution of bed density in gas–solid fluidized bed under different ratios of dense medium and gas velocities was investigated. Based on the data of this study and other scholars, the corresponding data set was constructed. A 13–14-1 bed density prediction model was established by using BP neural network algorithm. The overall correlation of the model is 0.9771. The model is more accurate to predict the bed density of single dense medium. The mean absolute percentage error is 4.17 % and the root mean square error is 0.0298. The optimized GA-BP (Genetic Algorithm-Back Propagation) neural network by genetic algorithm results are closer to the actual experimental values. The overall correlation of the model is increased to 0.9793, and the prediction accuracy can reach more than 90 %. The GA-BP neural network prediction model is more accurate in predicting fluidized beds with density fluctuation range of 1.6–2.2 g/cm3, which can meet the needs of industrial applications.

A Pattern Recognition Method for Filter Bags in Bag Dust Collectors Based on Φ-Optical Time-Domain Reflectometry

The use of phase-sensitive optical time-domain reflectometry (Φ-OTDR)-distributed fiber vibration sensors to detect and identify damaged bags in bag dust collectors has the potential to overcome the inadequacy of traditional damaged bag detection methods. In our previous study, we verified the feasibility of applying this technique in the field of damaged bag detection in bag filters. However, many problems still occur in engineering applications when using this technology to detect and identify damaged filter bags in pulse-jet dust-cleaning bag dust collectors. Further studies are needed to characterize the fiber vibration signals inside different types of rectangular damaged filter bags. A filter bag damage identification and detection method based on empirical mode decomposition (EMD) and a backpropagation (BP) neural network is proposed. The signal feature differences between intact filter bags and damaged filter bags with different rectangular hole sizes and positions are comparatively analyzed, and optimal feature difference parameters are proposed. Support vector machine (SVM) and a BP neural network are used to recognize different types of filter bag signals, and the comparison results show that the BP neural network algorithm is better at recognizing different types of filter bags, obtaining the highest recognition rate of 97.3%.

Model for Technology Risk Assessment in Commercial Banks

As the complexity of banking technology systems increases, the prevention of technological risk becomes an endless battle. Currently, most banks rely on the experience and subjective judgement of experts and employees to allocate resources for technological risk management, which does not effectively reduce the frequency of technology-related incidents. Through an analysis of mainstream risk management models, this study proposes a technology-based risk assessment system based on machine learning. It first identifies risk factors in bank IT, preprocesses the sample data, and uses different regression prediction models to train the processed data to build an intelligent assessment model. The experimental results indicated that the Genetic Algorithm–Backpropagation Neural Network model achieved the best performance. Based on assessment indicators, indicator weight values, and risk levels, commercial banks can develop targeted prevention and control measures by applying limited resources to the most critical corrective actions, thereby effectively reducing the frequency of technology-related incidents.

Comparative Analysis of Convolutional Neural Network-Long Short-Term Memory, Sparrow Search Algorithm-Backpropagation Neural Network, and Particle Swarm Optimization-Extreme Learning Machine Models for the Water Discharge of the Buzău River, Romania

Modeling and forecasting the river flow is essential for the management of water resources. In this study, we conduct a comprehensive comparative analysis of different models built for the monthly water discharge of the Buzău River (Romania), measured in the upper part of the river’s basin from January 1955 to December 2010. They employ convolutional neural networks (CNNs) coupled with long short-term memory (LSTM) networks, named CNN-LSTM, sparrow search algorithm with backpropagation neural networks (SSA-BP), and particle swarm optimization with extreme learning machines (PSO-ELM). These models are evaluated based on various criteria, including computational efficiency, predictive accuracy, and adaptability to different training sets. The models obtained applying CNN-LSTM stand out as top performers, demonstrating a superior computational efficiency and a high predictive accuracy, especially when built with the training set containing the data series from January 1984 (putting the Siriu Dam in operation) to September 2006 (Model type S2). This research provides valuable guidance for selecting and assessing river flow prediction models, offering practical insights for the scientific community and real-world applications. The findings suggest that Model type S2 is the preferred choice for the discharge forecast predictions due to its high computational speed and accuracy. Model type S (considering the training set recorded from January 1955 to September 2006) is recommended as a secondary option. Model type S1 (with the training period January 1955–December 1983) is suitable when the other models are unavailable. This study advances the field of water discharge prediction by presenting a precise comparative analysis of these models and their respective strengths

Using Neural Networks (Back Propagation Network) to Determine the Variables Affecting Water Pollution in the Euphrates River

With the economy's steady growth and rapid development of modern industrial technology, environmental problems have increased. Water pollution is one of the complex problems that most countries of the world suffer from, especially developing countries, because it contains various pollutants, some of which can decompose as organic pollutants, and some of which are difficult to decompose, such as toxic heavy metals. In Iraq, according to the Iraqi Ministry of Environment - Water Pollution Division statistics for the year 2018, specifically the waters of the Euphrates River, a group of variables affecting the water quality of this river has been identified. The scarcity of water and the accumulation of various wastes in the water networks and on the banks of the Euphrates River also contributed greatly to the increase in the problem of pollution. The continued poor quality of the water has led to severe environmental health concerns. The research aims to shed light on the most important variables affecting the pollution of the waters of the Euphrates River and the water quality and quality in this river. The principal components analysis and the neural network (backpropagation algorithm) were used to determine the variables that affect the pollution of the Euphrates River water. They were applied to real data from the Ministry of Environment for the Euphrates water pollution. One of the most important conclusions we reached when using statistical methods was that the variable (DO2) is not significant and does not affect the pollution of the Euphrates River either. Still, When Using the algorithm, we get that all these variables (TH, TDS, EC) do not affect the water pollution of the Euphrates River.

Research on dynamic splitting tensile damage characteristics of steel fiber reinforced concrete under freeze-thaw cycle environment based on AE

This paper aims to investigate the dynamic splitting tensile damage characteristics of steel fiber reinforced concrete (SFRC) and achieve early warning of the dangerous stage under freeze-thaw cycle environment through acoustic emission (AE) technology, Mel-frequency cepstral coefficients (MFCCs) and genetic algorithm-back propagation (GA-BP) neural network. The effects of fiber content and loading rate on mechanical properties, wavelet energy spectrum coefficient and wavelet time-frequency-energy maximum, as well as MFCCs of AE signals were studied herein. Then a GA-BP neural network was established to realize the classification of the damage degree of SFRC under freeze-thaw cycling with MFCCs as the signal characteristic. The results indicate that the increasing loading rate leads to more serious brittle failure, but the appropriate steel fiber content can make the cracks generated develop fully during the dynamic splitting tensile failure process by analyzing the wavelet energy spectrum coefficient and wavelet time-frequency-energy maximum. It is also found that the damage degree of SFRC can be identified by analyzing the MFCCs. Furthermore, the GA-BP neural network performs well in the classification of the damage degree of SFRC with the recognition over 90%, which is more suitable for concrete conditions with a proper loading rate and fiber content.

Prediction of Converter Tapping Weight Based on Principal Component Analysis–Whale Optimization Algorithm–Backpropagation Algorithm Neural Network Model

Tapping weight is an important parameter in converter blowing process, wherein precisely predicting the quantity of steel required in an alloy baking converter can effectively guide the requirement of alloy ingredients. In practical production, the main approach is empirical estimation, despite its low accuracy. Employing a general neural network model for prediction requires to gather the converter blowing parameters and endpoint temperature measurement sampling parameters as the model inputs. However, this data cannot be obtained until the blowing process reaches its endpoint, rendering it impractical for alloy batching that requires advance preparation for baking. In this study, a principal component analysis–whale optimization algorithm–backpropagation algorithm (PCA–WOA–BP) neural network tapping prediction model is developed using raw material parameters available before alloy baking as input. This model is integrated into the intelligent alloy reduction model of a factory. The model achieves a regression coefficient R2 of 0.823, with 98.53% of furnaces having a prediction error of less than 2 t. The tapping weight of 20 consecutive heats in actual production is predicted; the error range is less than 80% within 1 t, and the converter tapping weight is predicted accurately.

Preschool Teachers Teaching Quality Evaluation Based on Neural Network Algorithms

Based on the current teaching situation of preschool teachers, in order to comprehensively evaluate the effectiveness of early childhood teaching, research have constructed a teaching quality evaluation model using fuzzy synthesis method and expert method. This model can handle the fuzzy relationship between evaluation indicators and achieve the evaluation of teaching quality. Considering that the teaching evaluation is influenced by many factors, the Genetic Algorithm back propagation (GA-BP) neural network algorithm is chosen for the solution model construction of the preschool teacher teaching quality. The entropy method chosen for the data calculation is to complete the preschool teaching quality evaluation. In the mean square error test for solving the model, the improved GA-BP model converged after 40 iterations with the model convergence speed increased by 34.65%. In the evaluation and prediction of preschool teacher indicators, the improved GA-BP model accurately evaluated the teacher classroom teaching indicators. In sample 3, sample 6, and sample 9, the improved GA-BP model scored 91, 89, and 88 points, respectively, close to the true scoring results. The improved model’s accuracy was high as 90% in teacher skills, personality charm, and academic research evaluation. The improved model also effected better in the application of the preschool teaching quality evaluation. The application effect of this model in teaching effectiveness evaluation and teacher quality evaluation is good, providing valuable reference for the establishment of early childhood education evaluation system.

Quantitative research on stress failure risk assessment for girth welds with unequal wall thickness of the X80 pipeline under lateral load

The cumulative length of the X80 steel pipeline extends to 17,000 km, with a predominant portion traversing mountainous terrains characterized by intricate geological formations. Recurrent geological hazards significantly impede the safe operation of oil and gas pipelines. Landslides can induce substantial axial forces on steel pipelines, significantly increasing the risk of pipeline failure. Moreover, as a result of sudden changes in mechanical structure, the pipeline's unequal wall thickness leads to local stress concentration, making it more vulnerable to external loads than the steel pipeline itself. Therefore, to analyze the mechanical response of steel pipelines under different working conditions, we establish a smooth particle hydrodynamics, finite element transverse landslide pipeline model for girth welds with unequal wall thickness and investigate a tearing accident caused by a lateral landslide in a section of the large-diameter natural gas pipeline. The landslide volume is found to be the main controlling factor for the failure of pipeline circumferential welds. According to the genetic algorithm backpropagation neural network, the von Mises stress of the most dangerous section of the pipeline is calculated based on pipeline and landslide parameters, and the error is 11.67 %. According to the API RP 581–2016 Risk-Based Inspection Methodology, a failure risk assessment matrix is established using the pipeline statute as an evaluation indicator to evaluate the landslide risk to pipelines in different regions. A variable-wall-thickness girth weld on the ZG pipeline was selected for application. This method has good applicability for evaluating the failure risk of circumferential welds with wall thicknesses of 12.8/15.3 mm under the action of small landslides.

Thermal management of methanol steam reforming reactor by back propagation neural network and particle swarm optimization algorithm

Methanol steam reforming (MSR) reactor is a device that converts the methanol into hydrogen. Since the MSR is an endothermic reaction, the methanol conversion is usually affected by the heating conditions. So, to improve the methanol conversion, the heating condition should be managed. This study aims to present a framework and methodology for studying the thermal management of a determined MSR reactor by controlling the heating conditions. Firstly, the characteristics of a three-dimensional MSR reactor are carried out, and the effects of the inlet condition parameters e.g. heating parameters and fuel supply parameters, on the methanol conversion are investigated. Then, the performance prediction of the MSR reactor is studied using several common intelligence algorithms and the corresponding surrogate models are obtained. The back propagation neural network (BPNN) surrogate model shows the best prediction accuracy than stepwise linear (SL), radial basis function neural network (RBFNN), linear support vector machines (L-SVM), and random forest algorithms (RFA). Finally, the thermal management of the MSR reactor is carried out by combining the surrogate model and particle swarm optimization (PSO) algorithm to obtain better performance. The results show that hydrogen production of the MSR reactor can be generally guaranteed by the characteristic curves of the heating parameters and fuel supply parameters, and the methanol conversion can be maintained steadily above 93% within the whole period of the hydrogen production requirements.

Optimisation of drag coefficient of a car based on back propagation neural network and genetic algorithm

Optimisation of drag coefficient of a car based on back propagation neural network and genetic algorithm

Fatigue Crack Growth Rate Prediction in Nickle based Super-alloys using Machine Learning Algorithm

The state-of-the-art research in any field is inclined towards the use of artificial intelligence (AI) and machine learning (ML) techniques due to their versatility and ease of application without compromise in the results. Predicting the service life of structural members subjected to fatigue loading, is a daunting task and is currently dependent on experimental, finite element (FE) and/or numerical approaches. Damage tolerant (DT) design incorporates fatigue crack growth rate (FCGR) test data to avoid catastrophic structural failure and is proven to be successful in predicting the life of a component. Such predictions also prove beneficial in applications such as structural health monitoring (SHM) and planning non-destructive inspection. Prediction methods and models, currently used, are application specific or inconsistent across different applications. Hence in the present work, an ML algorithm is proposed to predict the Paris region of the FCGR curve for nickel based super alloys, viz. GTM 720 and GTM 718. Experimental dataset for various stress ratios (R) of the alloys were used to train the back propagation neural network (BPNN) algorithm. The algorithm was appropriately tuned and evaluated so as to fit the training data well and achieve good generalization. Using this trained ML algorithm, the FCGR was predicted for a different stress ratio and compared with experimental results of GTM 720 and GTM 718, available in literature. The results obtained are quite comparable indicating promising applications for ML based algorithms in FCGR prediction.