Back Propagation Neural Network Algorithm Research Articles

Prediction models for retinopathy of prematurity occurrence based on artificial neural network

IntroductionEarly prediction and timely treatment are essential for minimizing the risk of visual loss or blindness of retinopathy of prematurity, emphasizing the importance of ROP screening in clinical routine.ObjectiveTo establish predictive models for ROP occurrence based on the risk factors using artificial neural network.MethodsA cohort of 591 infants was recruited in this retrospective study. The association between ROP and perinatal factors was analyzed by univariate analysis and multivariable logistic regression. We developed predictive models for ROP screening using back propagation neural network, which was further optimized by applying genetic algorithm method. To assess the predictive performance of the models, the areas under the curve, sensitivity, specificity, negative predictive value, positive predictive value and accuracy were used to show the performances of the prediction models.ResultsROP of any stage was found in 193 (32.7%) infants. Twelve risk factors of ROP were selected. Based on these factors, predictive models were built using BP neural network and genetic algorithm-back propagation (GA-BP) neural network. The areas under the curve for prediction models were 0.857, and 0.908 in test, respectively.ConclusionsWe developed predictive models for ROP using artificial neural network. GA-BP neural network exhibited superior predictive ability for ROP when dealing with its non-linear clinical data.

Structural Optimization of a Giant Magnetostrictive Actuator Based on BP-NSGA-II Algorithm

This study introduces an integrated structural optimization design method based on a BP neural network and NSGA-II multi-objective genetic algorithm. Initially, a two-dimensional axisymmetric finite element model of the Giant Magnetostrictive Actuator (GMA) was established, and the coupling simulation of the electromagnetic field, structural field, and temperature field was conducted to obtain the GMA’s performance parameters. Subsequently, the structural parameters of the GMA magnetic circuit, including the magnetic conducting ring, magnetic conducting sidewall, magnetic conducting body, and coil, were used as inputs, and the axial magnetic induction intensity, uniformity of axial magnetic induction intensity, and coil loss on the Giant Magnetostrictive Material (GMM) rod were used as outputs to establish a back propagation (BP) neural network model. This model delineated the nonlinear relationship between structural parameters and performance parameters. Then, the BP-NSGA-II algorithm was applied to perform multi-objective optimization on the actuator’s structural parameters, resulting in a set of Pareto optimal non-dominated solutions, from which a set of optimal solutions was obtained using the entropy weight method. Finally, simulation analysis of this optimal solution was conducted, indicating that under a 5 A power supply excitation, the maximum axial magnetic induction intensity on the optimized GMM rod increased from 0.87 T to 1.12 T; the uniformity of axial magnetic induction intensity improved from 93.1% to 96.5%; and the coil loss decreased from 7.79 × 104 W/m3 to 4.97 × 104 W/m3. Based on the optimization results, a prototype actuator was produced, and the test results of the prototype’s output characteristics proved the feasibility of this optimization design method.

Reliable prediction of peak cladding temperature during top reflooding processes: A neural network-based perception-interaction approach

The reliable prediction of peek cladding temperature is crucial for heat transfer and reactor security when large break loss of coolant accident (LBLOCA) happens. This study proposes a neural network-based Perception-Interaction system to effectively predict and monitor the peak cladding temperature during reflooding phase after LOCA. The system employs a narrow rectangular channel for conducting top reflooding experiments, wherein the peak temperature is predicted by the Back Propagation Neural Network (BPNN) algorithm. The system’s primary objective is to accurately predict the peak cladding temperature of various test conditions. First, the special Counter-Current Flow Limitation (CCFL) phenomenon is identified based on the different heat exchange patterns inside the test section. Then the experimental data is collected by LabVIEW and calculated by MATLAB to accurately predict the quench velocity and peak temperature at a specific location by conducting various test conditions. The final experiment results indicate that the built Perception-Interaction system in this article has a high prediction accuracy, with a relative error within 2% between the predicted peak temperatures and actual temperatures.

Assessment and Enhancement of Chinese College Students’ Cross- Cultural Learning Competence Based on BP Neural Network Algorithm

Cross-cultural learning competence, a critical skill in our globally interconnected world, is advanced through the application of the Backpropagation (BP) neural network algorithm. This innovative approach involves leveraging neural network techniques to model and enhance individuals' abilities to navigate and understand diverse cultural contexts. The BP neural network algorithm facilitates personalized learning experiences by adapting to individuals' cultural backgrounds and preferences. This research explores a comprehensive approach for assessing and enhancing cross-cultural learning competence among Chinese college students, integrating the Word Embedding Multilingual Model with the Back Propagation Neural Network (WEMM-BPNN) algorithm. Recognizing the importance of global competencies in higher education, our study focuses on leveraging advanced neural network techniques to evaluate and elevate students' cross-cultural learning abilities. The WEMM-BPNN model combines the power of word embedding and multilingual considerations, tailoring the learning experience to individual cultural backgrounds. Through a meticulous analysis of cross-cultural data and linguistic patterns, the algorithm refines its recommendations for personalized learning strategies. The research aims not only to assess the current state of cross-cultural learning competence but also to provide targeted interventions to enhance students' intercultural understanding and adaptability. By merging linguistic models with neural network algorithms, this study offers a pioneering approach to cultivating cross-cultural competencies, contributing valuable insights to the ongoing discourse on globalized education.

Predicting green tea moisture content during roasting by shape feature

This study investigated the changes in tea moisture content and shape features parameters during the roasting process of roasted green tea under different temperatures(70, 100 and 130 °C), leaf amounts (40, 70 and 100 kg), and cylinder speeds (25, 27.5 and 30 r/min), and developed a moisture content prediction model based on Random forest-Genetic algorithm-Back propagation(RF-GA-BP) neural network. Firstly, based on image processing technology, the shape parameters of tea leaves during the roasting process were automatically extracted, and the random forest algorithm was used to rank their importance to reduce the complexity of the model. Then, the activation function and the number of hidden layer nodes in the network structure were optimized. Finally, the initial weights and thresholds of the neural network were optimized using genetic algorithm to obtain the optimal parameters for constructing a moisture content prediction model during the roasting process. The results indicated that the importance of slightness was the highest, with a value of 0.865. The RF-GA-BP neural network model with mean absolute error, root mean square error, and coefficient of determination of 0.030, 0.036, and 0.915, respectively, was superior to the BP, GA-BP, and RF-BP neural network models, and could better predict the moisture content of tea during the roasting process.

Performance prediction and optimization of lateral exhaust hood based on back propagation neural network and genetic algorithm

The lateral exhaust hood (LEH) is commonly used to capture contaminated airflow in industrial plants. Its performance is influenced by numerous factors and exhibits a strong nonlinear relationship, optimizing this performance demands significant computational time and cost. This study proposes an LEH optimization design method that combines a backpropagation neural network (BPNN) with a genetic algorithm (GA), verified through experiments and numerical simulations. A BPNN prediction model is established based on 520 CFD simulation results. This study discusses seven factors influencing LEH performance, including the buoyant jet's velocity and temperature, the LEH's geometry and position, and the exhaust velocity. The results indicate that within the parameter range of the prediction model, there is a critical aspect ratio value that significantly affects LEH capture efficiency. The horizontal distance between the LEH and the pollution source significantly affects capture efficiency, especially when the LEH's installation height is low (H/D < 0.5). In addition, when combined with GA for fast searching, the expected combination of LEH design parameters can be obtained. This study aims to enhance the design efficiency of industrial exhaust hoods and improve worker health protection.

Prediction of Short-Term Winter Photovoltaic Power Generation Output of Henan Province Using Genetic Algorithm–Backpropagation Neural Network

Prediction of Short-Term Winter Photovoltaic Power Generation Output of Henan Province Using Genetic Algorithm–Backpropagation Neural Network

Simulation of Optimization Decision Model for Low-carbon Economy Schemes Based on Machine Learning Algorithms

Low-carbon economy (LCE) is a shift from high-carbon intensive industries and activities to low-emission or zero-emission industries and activities, which requires a lot of investment in new technologies and infrastructure, as well as changes in policies and regulations to promote sustainability and reduce emissions. In this article, the back propagation neural network (BPNN) algorithm in machine learning (ML) is applied to the construction of low-carbon economic scheme optimization decision model, and the performance of the model is simulated and tested. The results show that BPNN algorithm has higher accuracy and reliability in predicting the development level of LCE, which is 23.8% higher than Support Vector Machine (SVM) algorithm. Through the comparative study of BPNN algorithm and SVM algorithm in forecasting the development level of LCE, the advantages of BPNN algorithm in forecasting accuracy and application value are verified. BPNN algorithm can better adapt to and deal with the complex data patterns in the field of LCE, improve the accuracy and reliability of prediction, and provide strong technical support and guarantee for the growth of LCE.

Laser-induced breakdown spectroscopy as a method for millimeter-scale inspection of surface flatness

A non-contact method for millimeter-scale inspection of material surface flatness via Laser-Induced Breakdown Spectroscopy (LIBS) is investigated experimentally. The experiment is performed using a planished surface of an alloy steel sample to simulate its various flatness, ranging from 0 to 4.4 mm, by adjusting the laser focal plane to the surface distance with a step length of 0.2 mm. It is found that LIBS measurements are successful in inspecting the flatness differences among these simulated cases, implying that the method investigated here is feasible. It is also found that, for achieving the inspection of surface flatness within such a wide range, when univariate analysis is applied, a piecewise calibration model must be constructed. This is due to the complex dependence of plasma formation conditions on the surface flatness, which inevitably complicates the inspection procedure. To solve the problem, a multivariate analysis with the help of Back-Propagation Neural Network (BPNN) algorithms is applied to further construct the calibration model. By detailed analysis of the model performance, we demonstrate that a unified calibration model can be well established based on BPNN algorithms for unambiguous millimeter-scale range inspection of surface flatness with a resolution of about 0.2 mm.

Improved BP Neural Network Algorithm for Predicting Structural Parameters of Mirrors

Addressing the nonlinear correlations between input variables and output responses, in addition to the time-consuming nature of finite element analysis in mirror design, this study introduces an enhanced back-propagation (BP) neural network (BR-TLDBO-BPNN) employing Bayesian regularization and an optimized dung beetle algorithm. This novel approach facilitates rapid and efficient parameter estimations, significantly reducing the computational overhead. Utilizing an integrated analysis platform, the study obtained training and test samples, and the BR-TLDBO-BPNN model is used to predict the reflector’s mass and root mean square (RMS). The optimization mathematical model is built, and the nonlinear planning function (fmincon) is utilized to solve the problem and find an ideal set of structural parameters. The outcomes demonstrate that the prediction model is accurate enough to predict the mirror characteristics to optimize the mirror structural parameters. Empirical validation demonstrates that the proposed model achieves an over 99% accuracy in predicting mirror characteristics against finite element simulations. As a result, the BR-TLDBO-BPNN algorithm studied in this article not only broadens the application scope of neural networks, but also provides a new practical technique for engineering design.

Identification of key biomarkers for early warning of diabetic retinopathy using BP neural network algorithm and hierarchical clustering analysis

Diabetic retinopathy is one of the most common microangiopathy in diabetes, essentially caused by abnormal blood glucose metabolism resulting from insufficient insulin secretion or reduced insulin activity. Epidemiological survey results show that about one third of diabetes patients have signs of diabetic retinopathy, and another third may suffer from serious retinopathy that threatens vision. However, the pathogenesis of diabetic retinopathy is still unclear, and there is no systematic method to detect the onset of the disease and effectively predict its occurrence. In this study, we used medical detection data from diabetic retinopathy patients to determine key biomarkers that induce disease onset through back propagation neural network algorithm and hierarchical clustering analysis, ultimately obtaining early warning signals of the disease. The key markers that induce diabetic retinopathy have been detected, which can also be used to explore the induction mechanism of disease occurrence and deliver strong warning signal before disease occurrence. We found that multiple clinical indicators that form key markers, such as glycated hemoglobin, serum uric acid, alanine aminotransferase are closely related to the occurrence of the disease. They respectively induced disease from the aspects of the individual lipid metabolism, cell oxidation reduction, bone metabolism and bone resorption and cell function of blood coagulation. The key markers that induce diabetic retinopathy complications do not act independently, but form a complete module to coordinate and work together before the onset of the disease, and transmit a strong warning signal. The key markers detected by this algorithm are more sensitive and effective in the early warning of disease. Hence, a new method related to key markers is proposed for the study of diabetic microvascular lesions. In clinical prediction and diagnosis, doctors can use key markers to give early warning of individual diseases and make early intervention.

Enhancing PEMEC Efficiency: A synergistic approach using CFD analysis and Machine learning for performance optimization

The proton exchange membrane electrolytic cell (PEMEC) is a clean, pollution-free, and promising device for producing hydrogen from water electrolysis. This paper enhances the electrochemical performance of the PEMEC by developing a comprehensive three-dimensional two-phase non-isothermal heat and mass transfer cell model. An analysis was conducted to study the influence of various operating parameters on the temperature safety and efficiency of the cell. The performance of the latter was optimized by adopting the machine learning technology, which aimed at identifying optimal conditions for improved performance. The obtained results showed that, when the inlet water flow rate and membrane thickness were increased, the average temperature of the anode catalyst layer (A-CL) was decreased by 1.4 K and 2.31 K, respectively. This allowed to improve the safety of the electrolytic cell. However, it would reduce the hydrogen and oxygen mole fraction in the electrolyzer. On the contrary, enhancing the porosity or thickness of the anode gas diffusion layer can increase the average A-CL temperature and decrease the oxygen and hydrogen mole fraction. Therefore, the backpropagation neural network and non-dominated sorting genetic algorithm were used to predict and optimize the performance of the electrolyzer, in order to maximize its temperature safety, electrochemical reaction rate, and hydrogen evolution efficiency as much as possible. The results of this study provide a theoretical foundation for the selection of suitable cell models according to different conditions in engineering applications. It also has an application value in energy saving and sustainable development.

Determinants influencing ecological footprints of China; An empirical evidence from factor analysis

China has advanced significantly in the last decades, but ecological concerns raise questions about the nation's long-term affluence. The hunt for possible determinants in explaining the quality of the environment (ecological footprint) is still in progress. Therefore, the current study attempts to investigate the influencing factors from various explanatory variables (eco-innovation, economic complexity, renewable energy consumption, economic growth, natural resource rent, foreign direct investment, the energy intensity level of primary energy, economic development, Public-private partnerships investment in energy, electric power consumption, and population growth) through factor analysis augmented with Back Propagation Neural Network (BPNN) approach. The annual data from 1995 to 2021 for China is retrieved for empirical analysis. The data set comprises the factors classified into training, validation, and testing divisions with a ratio of 2:1:1. The outcomes revealed that extracted factors are robust in explaining China's ecological footprint variation. Outcomes of the BPNN algorithm suggest that renewable energy consumption, eco-innovation, economic complexity, natural resource rent, and electric power consumption are the most useful determinants in explaining the ecological footprints of China. The study furnishes the possible policy implications of the research.

Lithium-Ion Battery Capacity Prediction with GA-Optimized CNN, RNN, and BP

Over the last 20 years, lithium-ion batteries have become widely used in many fields due to their advantages such as ease of use and low cost. However, there are concerns about the lifetime and reliability of these batteries. These concerns can be addressed by obtaining accurate capacity and health information. This paper proposes a method to predict the capacity of lithium-ion batteries with high accuracy. Four key features were extracted from current and voltage data obtained during charge and discharge cycles. To enhance prediction accuracy, the Pearson correlation coefficient between these features and battery capacities was analyzed and eliminations were made for some batteries. Using a genetic algorithm (GA), the parameter optimization of Convolutional Neural Network (CNN), Backpropagation (BP), and Recurrent Neural Network (RNN) algorithms was performed. The parameters that provide the best performance were determined in a shorter time using GA, which includes natural selection and genetic processes instead of a trial-and-error method. The study employed five metrics—Mean Square Error (MSE), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Mean Absolute Error (MAE), and Squared Correlation (R2)—to evaluate prediction accuracy. Predictions based on NASA experimental data were compared with the existing literature, demonstrating superior accuracy. Using 100 training data, 68 data predictions were made with a Root Mean Square Error (RMSE) of 0.1176%. This error rate represents an accuracy level 2.5 times higher than similarly accurate studies in the literature.

Retraction: The deformation monitoring of foundation pit by back propagation neural network and genetic algorithm and its application in geotechnical engineering

Retraction: The deformation monitoring of foundation pit by back propagation neural network and genetic algorithm and its application in geotechnical engineering

Prediction of Drilling Efficiency for Rotary Drilling Rig Based on an Improved Back Propagation Neural Network Algorithm

Accurately predicting the drilling efficiency of rotary drilling is the key to achieving intelligent construction. The current types of principle analysis (based on traditional interactive experimental methods) and efficiency prediction (based on simulation models) cannot meet the requirements needed for the efficient, real-time, and accurate drilling efficiency predictions of rotary drilling rigs. Therefore, we adopted a method based on machine learning to predict drilling efficiency. The extremely complex rock fragmentation process in drilling conditions also brings challenges to predicting drilling efficiency. Therefore, this article went through a combination of mechanism and data analysis to conduct correlation analysis and to clarify the drilling characteristic parameters that are highly correlated with drilling efficiency, and it then used them as inputs for machine learning models. We propose a rotary drilling rig drilling efficiency prediction model based on the GA-BP neural network to construct an accurate and efficient drilling efficiency prediction model. Compared with traditional BP neural networks, it utilizes the global optimization ability of a genetic algorithm to obtain the initial weights and thresholds of a BP neural network in order to avoid the defect of ordinary BP neural networks, i.e., that they easily fall into local optimal solutions during the training process. The average prediction accuracy of the GA-BP neural network is 93.6%, which is 3.1% higher than the traditional BP neural network.

Alignment Prediction of Air Spring Vibration Isolation System for Marine Shafting Based on Genetic Algorithm-Back Propagation Neural Network.

Shafting alignment plays an important role in the marine propulsion system, which affects the safety and stability of ship operation. Air spring vibration isolation systems (ASVISs) for marine shafting can not only reduce mechanical noise but also help control alignment state by actively adjusting air spring pressures. Alignment prediction is the first and a key step in the alignment control of ASVISs. However, in large-scale ASVISs, due to factors such as strong interference and raft deformation, alignment prediction faces problems such as alignment measurement sensors failure and difficulty in establishing a mathematical model. To address this problem, a data model for predicting alignment state is developed based on a back propagation (BP) neural network, fully taking advantage of its self-learning and self-adaption abilities. The proposed model exploits the collected data in the ASVIS instead of the alignment measurement data to calculate the alignment state, providing another alignment prediction approach. Then, in order to solve the local optimum issue of BP neural network, we introduce the genetic algorithm (GA) to optimize the weights and thresholds of the BP neural network, and an improved GA-BP model is designed. The GA-BP model can leverage the advantages of the global search capability of GA as well as the BP neural network's fast convergence in local search. Finally, we conduct experiments on a real ASVIS and evaluate the prediction models using different criteria. The experimental results show that the proposed prediction model with the GA-BP neural network can accurately predict the alignment state, with a mean-square error (MSE) of 0.0114. And compared to the BP neural network, the GA-BP neural network reduces the MSE by approximately 74%.

Application of Predictions for the Total Productivity of Competency Certificates Electrical Engineering Staff at PT. Eleska Iatki Using Artificial Neural Network Backpropagation Algorithm

Application of Predictions for the Total Productivity of Competency Certificates Electrical Engineering Staff at PT. Eleska Iatki Using Artificial Neural Network Backpropagation Algorithm

Optimization of the Welding Parameters of High-Quality Aluminum/Copper FSSW Joints Using Taguchi Method Combined with Back Propagation Neural Network and Genetic Algorithm

Optimization of the Welding Parameters of High-Quality Aluminum/Copper FSSW Joints Using Taguchi Method Combined with Back Propagation Neural Network and Genetic Algorithm

Prediction of Physical Ability of College Students Based on the BP Neural Network

The prediction of physical ability is a key point to understand the physical training effect of college students. This paper uses the error Back Propagation neural network algorithms to investigate the college students&amp;rsquo; physical test results, and predicts the future trends of the results. The findings indicate that, in future ten years, the physical test grades of both men and women will slip to a certain extent. It implies that the indicators of speed, power, endurance level and physical strength of college students keep going down. This phenomenon should be paid attention to. It is necessary to strength the exercise consciousness, and stimulates the enthusiasm of students&amp;rsquo; exercise.