Back Propagation Neural Network Algorithm Research Articles

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.

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.

Freeway Merging Trajectory Prediction for Automated Vehicles Using Naturalistic Driving Data

Due to differences in speed and the complex interaction between merging and through-lane vehicles at freeway merging sections, crashes involving both human drivers and automated vehicles (AVs) persist. To assist AVs to predict the intentions of surrounding vehicles for further dynamic motion planning, researchers have focused on developing trajectory prediction algorithms. Few studies, however, have developed merging trajectory prediction models using naturalistic driving data in China, making it urgent to put it on the agenda for AVs’ safety and efficiency at freeway merging sections. Based on merging periods extracted from the Shanghai Naturalistic Driving Study (SH-NDS), this study compared merging behavior on freeways with through-lane speed limits of 80 km/h, 100 km/h, and 120 km/h using analysis of variance (ANOVA). Merging trajectory prediction algorithms for these three speed limit cases were trained and tested using backpropagation neural network (BPNN) and long short-term memory neural network (LSTM NN) approaches. Results show 1) significant differences among the three cases in all merging behavior variables except for longitudinal gap; and 2) the BPNN algorithm for merging trajectory prediction demonstrated superior performance compared to the LSTM NN. Two major contributions to the safe operation of AVs are provided: 1) the developed algorithms can be integrated into AV systems to accurately predict real-time desired trajectories of nearby merging vehicles in uncongested traffic conditions, and assist ongoing motion planning strategies for AVs; and 2) the algorithms can be incorporated in simulation tests for AV safety evaluation involving freeway merging sections.

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.

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’ 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’ exercise.

Optimizing Active Disturbance Rejection Control for a Stubble Breaking and Obstacle Avoiding Control System

In order to improve the obstacle avoidance control performance and anti-interference ability of a stubble breaking device of a no-tillage planter, a back-propagation neural network (BPNN)-optimized fuzzy active disturbance rejection control (ADRC) controller was designed to optimize the control performance of a servo motor. Firstly, a negative feedback mathematical model was established for the obstacle avoidance control system. Then, the nonlinear state error feedback (NLSEF) parameters in the fuzzy ADRC were intelligently optimized by the BPNN algorithm. In this way, a fuzzy ADRC controller based on BPNN optimization was formed to optimize the control process of a servo motor. Matlab/Simulink (R2022b) was used to complete the simulation model design and parameter adjustment. Consequently, the response time was 0.089 s using the BPNN fuzzy ADRC controller, which was shorter than the 0.303 s of the ADRC controller and the 0.100 s of the fuzzy ADRC controller. The overshoot was 0.1% using a BPNN fuzzy ADRC controller, which was less than the 2% of the ADRC controller and the 1% of the fuzzy ADRC controller. After noise signal interference was introduced into the control system, the regression steady state time of the BPNN fuzzy ADRC controller was 0.22 s, which was shorter than the 0.56 s of the ADRC controller and the 0.45 s of the fuzzy ADRC controller. A hardware-in-the-loop simulation experimental platform of the obstacle avoidance control system was constructed. The experiment results show that the servo motor control system has a fast dynamic response, small steady-state error and strong anti-interference ability for obstacle avoidance at the target height. Then, the control system error was within the allowable range. The servo motor control effect of the BPNN fuzzy ADRC was better than the ADRC and fuzzy ADRC. This optimized servo motor control method can provide a reference for improving the obstacle avoidance control effect problem of no-tillage seeders in stubble breaking operations on rocky desertification areas.

Height Prediction of Water-Conducting Fracture Zone in Jurassic Coalfield of Ordos Basin Based on Improved Radial Movement Optimization Algorithm Back-Propagation Neural Network

The development height of the water-conducting fracture zone (WCFZ) is crucial for the safe production of coal mines. The back-propagation neural network (BP-NN) can be utilized to forecast the WCFZ height, aiding coal mines in water hazard prevention and control efforts. However, the stochastic generation of initial weights and thresholds in BP-NN usually leads to local optima, which might reduce the prediction accuracy. This study thus invokes the excellent global optimization capability of the Improved Radial Movement Optimization (IRMO) algorithm to optimize BP-NN. The influences of mining thickness, coal seam depth, working width, and hard rock lithology proportion coefficient on the height of WCFZ are investigated through 75 groups of in situ data of WCFZ heights measured in the Jurassic coalfield of the Ordos Basin. Consequently, an IRMO-BP-NN model for predicting WCFZ height in the Jurassic coalfield of the Ordos Basin was constructed. The proposed IRMO-BP-NN model was validated through monitoring data from the 4−2216 working faces of Jianbei Coal Mine, followed by a comparative analysis with empirical formulas and conventional BP-NN models. The relative error of the IRMO-BP-NN prediction model is 4.93%, outperforming both the BP-NN prediction model, the SVR prediction model, and empirical formulas. The results demonstrate that the IRMO-BP-NN model enhances the accuracy of predicting WCFZ height, providing an application foundation for predicting such heights in the Jurassic coalfield of the Ordos Basin and protecting the ecological environment of Ordos Basin mining areas.

A novel hybrid model for species distribution prediction using neural networks and Grey Wolf Optimizer algorithm

Neural networks are frequently employed to model species distribution through backpropagation methods, known as backpropagation neural networks (BPNN). However, the complex structure of BPNN introduces parameter settings challenges, such as the determination of connection weights, which can affect the accuracy of model simulation. In this paper, we integrated the Grey Wolf Optimizer (GWO) algorithm, renowned for its excellent global search capacity and rapid convergence, to enhance the performance of BPNN. Then we obtained a novel hybrid algorithm, the Grey Wolf Optimizer algorithm optimized backpropagation neural networks algorithm (GNNA), designed for predicting species’ potential distribution. We also compared the GNNA with four prevalent species distribution models (SDMs), namely the generalized boosting model (GBM), generalized linear model (GLM), maximum entropy (MaxEnt), and random forest (RF). These models were evaluated using three evaluation metrics: the area under the receiver operating characteristic curve, Cohen’s kappa, and the true skill statistic, across 23 varied species. Additionally, we examined the predictive accuracy concerning spatial distribution. The results showed that the predictive performance of GNNA was significantly improved compared to BPNN, was significantly better than that of GLM and GBM, and was even comparable to that of MaxEnt and RF in predicting species distributions with small sample sizes. Furthermore, the GNNA demonstrates exceptional powers in forecasting the potential non-native distribution of invasive plant species.

Monitoring of Chlorophyll Content of Potato in Northern Shaanxi Based on Different Spectral Parameters.

Leaf chlorophyll content (LCC) is an important physiological index to evaluate the photosynthetic capacity and growth health of crops. In this investigation, the focus was placed on the chlorophyll content per unit of leaf area (LCCA) and the chlorophyll content per unit of fresh weight (LCCW) during the tuber formation phase of potatoes in Northern Shaanxi. Ground-based hyperspectral data were acquired for this purpose to formulate the vegetation index. The correlation coefficient method was used to obtain the "trilateral" parameters with the best correlation between potato LCCA and LCCW, empirical vegetation index, any two-band vegetation index constructed after 0-2 fractional differential transformation (step size 0.5), and the parameters with the highest correlation among the three spectral parameters, which were divided into four combinations as model inputs. The prediction models of potato LCCA and LCCW were constructed using the support vector machine (SVM), random forest (RF) and back propagation neural network (BPNN) algorithms. The results showed that, compared with the "trilateral" parameter and the empirical vegetation index, the spectral index constructed by the hyperspectral reflectance after differential transformation had a stronger correlation with potato LCCA and LCCW. Compared with no treatment, the correlation between spectral index and potato LCC and the prediction accuracy of the model showed a trend of decreasing after initial growth with the increase in differential order. The highest correlation index after 0-2 order differential treatment is DI, and the maximum correlation coefficients are 0.787, 0.798, 0.792, 0.788 and 0.756, respectively. The maximum value of the spectral index correlation coefficient after each order differential treatment corresponds to the red edge or near-infrared band. A comprehensive comparison shows that in the LCCA and LCCW estimation models, the RF model has the highest accuracy when combination 3 is used as the input variable. Therefore, it is more recommended to use the LCCA to estimate the chlorophyll content of crop leaves in the agricultural practices of the potato industry. The results of this study can enhance the scientific understanding and accurate simulation of potato canopy spectral information, provide a theoretical basis for the remote sensing inversion of crop growth, and promote the development of modern precision agriculture.

Performance prediction and operating parameters optimization for proton exchange membrane fuel cell based on data-driven surrogate model and particle swarm optimization

The operating parameters of proton exchange membrane fuel cell (PEMFC) are critical to its performance and working life. This study presents a data-driven modeling approach combining a surrogate model with the particle swarm optimization algorithm to optimize operating parameters for PEMFC and obtain the maximum power density. The results show that the operating parameters significantly influence power density under high current densities, with inlet temperature having the most significant effect. Lower inlet temperature, relative humidity in the cathode and anode, along with higher operating pressure yield improved output performance. The Genetic Algorithm-Backpropagation Neural Network based surrogate model exhibits excellent predictive performance with correlation coefficients of 0.99896 and 0.99815 for the training and test sets, respectively. Optimized conditions achieve a 3.3% increase in power density compared to initial settings, with only a 0.15% error in simulation calculations. This data-driven approach provides valuable insights for maximizing PEMFC efficiency and performance.

Integrated database of granular soils under triaxial shear and its application in the prediction of stress–strain relationship

This study presents a novel data generation framework that generates a large database for machine learning (ML)-based soil model predictions. The dataset comprised 216 sets of triaxial tests on morphologically mutated and gene-decayed granular samples. This database was then estimated using five widely utilized ML algorithms to predict the stress-strain relationship of granular soils. They include the support vector machine (SVM), bagged trees, Gaussian process regression (GPR), and back propagation neural network (BPNN) algorithms. Following the hyperparameter settlement, model training, and testing, all the trained models captured the effects of the multiscale particle morphology, initial packing state, and confining stress. The excellent training and testing performances indicate the superior quality of the generated dataset. The fine tree, exponential GPR, and BPNN outperformed the Gaussian SVM and bagged trees in terms of the predictive performance. Among them, the exponential GPR exhibits the best model performance in reflecting the particle morphology effect, whereas the fine tree and BPNN generally exhibit good predictive performance for missing local information. Furthermore, all the models are tested by the micro-tomography (μCT) experimental data. The findings of this study were validated through a comparison between the DEM and model prediction results.

Electric power prediction of a two-body hinge-barge wave energy converter using machine learning techniques

Investigating the electric power generated by wave energy converters (WECs) has attracted increasing attention along with the developments of wave energy converting technologies in recent years. Electric power estimation currently depends on wave-to-wire model-based numerical analysis and experimental studies of scale-down prototypes. However, traditional numerical simulations are time-consuming and expensive for power orediction of a full-scale WECs operated in real sea. More importantly, they cannot accurately predict WECs’ power performance in real time, thereby cannot be used for prediction-based real-time control. To address those problems, four machine learning techniques are employed as an alternative to traditional, complex physical modelling processes to evaluate and predict the generated electric power, based on historical experimental data of a two-body hinge-barge WEC. Comparative results of a Backpropagation (BP) Neural Network algorithm, a Long Short-Term Memory (LSTM) algorithm, a Support Vector Machine (SVM) algorithm and a Radial Basis Neural Network (RBFF) algorithm indicate that machine-learning-based approaches can accurately predict the electric performance of this complex multi-degree-of-freedom WEC, where the coefficient of determination (R2) of the BP Neural Network on the test dataset is up to 0.9987.

Optimization Based on Computational Fluid Dynamics and Machine Learning for the Performance of Diffuser-Augmented Wind Turbines with Inlet Shrouds

A methodology that could reduce computational cost and time, combining computational fluid dynamics (CFD) simulations, neural networks, and genetic algorithms to determine a diffuser-augmented wind turbine (DAWT) design is proposed. The specific approach used implements a CFD simulation validated with experimental data, and key parameters are analyzed to generate datasets for the relevant mathematical model established with the backpropagation neural network algorithm. Then, the mathematical model is used with the non-dominant sorting genetic algorithm II to optimize the design and improve the DAWT design to overcome negative constraints such as noise and low energy density. The key parameters adopted are the diffuser’s flange height/angle, the diffuser’s length, and the rotor’s axial position. It was found that the impact of the rotor’s axial position on the power output of the DAWT is the most significant parameter, and a well-designed diffuser requires accelerating the airflow while maintaining high-pressure recovery. Introducing a diffuser can suppress the wind turbine’s noise, but if the induced tip vortex is too strong, it will have the opposite effect on the noise reduction.

Tight carbonate reservoir evaluation case study based on neural network assisted fracture identification and analytic hierarchy process

AbstractComprehensive evaluation of reservoirs is an important link in gas reservoir exploration and development. The evaluation of tight carbonate reservoirs often focuses on the characteristics of porosity and permeability, ignoring the important factor of fractures, also the quantitative evaluation of reservoirs is relatively few. It is difficult to identify fractures and evaluate the reservoir factors qualitatively and quantitatively. Herein, the sedimentary microfacies and microporosity of the tight carbonate reservoir of the Ma55 submember in the eastern Sulige area are comprehensively studied by casting thin section, rock physical property, and capillary pressure test data. The backpropagation (BP) neural network algorithm is used to identify and predict fractures. Finally, through the analytic hierarchy process, the above reservoir influencing factors are modeled and quantitatively analyzed for reservoir evaluation. The results show that the highest probability of fracture development in the central and northwest areas of the study area can reach 0.92. The accuracy of the BP neural network model in identifying cracks can reach 80%, which is reliable and effective compared with the conventional logging identification method. Reservoirs can be classified into four types according to their quality. The synthetic weights of porosity, permeability and fracture development probability are 0.2, 0.2 and 0.216 respectively, which are the three most important evaluation parameters. This study improves the accuracy of fracture identification and prediction of tight reservoirs in comprehensive reservoir evaluation, which provides guidance and scheme for more detailed exploration and development of tight carbonate reservoirs.