Levenberg-Marquardt Back Propagation Neural Network Research Articles

Simulation of fractional order mathematical model of robots for detection of coronavirus using Levenberg–Marquardt backpropagation neural network

Simulation of fractional order mathematical model of robots for detection of coronavirus using Levenberg–Marquardt backpropagation neural network

Enhanced seagull optimization for enhanced accuracy in CUDA-accelerated Levenberg–Marquardt backpropagation neural networks for earthquake forecasting

Hyperparameter tuning is crucial for enhancing the accuracy and reliability of artificial neural networks (ANNs). This study presents an optimization of the Levenberg–Marquardt backpropagation neural network (LM-BPNN) by integrating an improved seagull optimization algorithm (ISOA). The proposed ISOA-LM-BPNN model is designed to forecast earthquakes in the Caribbean region. The study further explores the impact of data and model parallelism, revealing that hybrid parallelism effectively mitigates the limitations of both. This leads to substantial gains in throughput and overall performance. To address computational demands, this model leverages the compute unified device architecture (CUDA) framework, enabling hybrid parallelism on graphics processing units (GPUs). This approach significantly enhances the model’s computational speed. The experimental results demonstrate that the ISOA-LM-BPNN model achieves a 20% improvement in accuracy compared to four baseline algorithms across three diverse datasets. The integration of ISOA with LM-BPNN refines the neural network’s hyperparameters, leading to more precise earthquake predictions. Additionally, the model’s computational efficiency is evidenced by a 56% speed increase when utilizing a single GPU, and an even greater acceleration with dual GPUs connected via NVLink compared to traditional CPU-based computations. The findings underscore the potential of ISOA-LM-BPNN as a robust tool for earthquake forecasting, combining high accuracy with enhanced computational speed, making it suitable for real-time applications in seismic monitoring and early warning systems.

A reliable neural network procedure for the novel sixth-order nonlinear singular pantograph differential model

An innovative singular nonlinear sixth-order (SNSO) pantograph differential model (PDM), known as the SNSO-PDM, is the subject of this novel study along with its numerical investigation. The concepts of pantograph and conventional Emden-Fowler have been presented in the design of the novel SNSO-PDM. The models based on Emden–Fowler have huge applications in mathematics and engineering and are always difficult to solve due to singularity. For each class of the innovative SNSO-PDM, the singularity, shape and pantograph factors are described. A reliable stochastic Levenberg-Marquardt backpropagation neural network (LMBPNN) procedure is designed for the SNSO-PDM. The correctness of the SNSOs-PDM is observed through the comparison performances of the achieved and reference outputs. The obtained results of the SNSO-PDM are considered by applying the process of training, certification, and testing to reduce the mean square error. To authenticate the efficacy of the innovative SNSO-PDM, the numerical performances of the solutions are depicted in the sense of regression, error histograms and correlation.

Classification of coal bursting liability of some chinese coals using machine learning methods

The classification of coal bursting liability (CBL) is essential for the mitigation and management of coal bursts in mining operations. This study establishes an index system for CBL classification, incorporating dynamic fracture duration (DT), elastic strain energy index (WET), bursting energy index (KE), and uniaxial compressive strength (RC). Utilizing a dataset comprising 127 CBL measurement groups, the impacts of various optimization algorithms were assessed, and two prominent machine learning techniques, namely the back propagation neural network (BPNN) and the support vector machine (SVM), were employed to develop twelve distinct models. The models’ efficacy was evaluated based on accuracy, F1-score, Kappa coefficient, and sensitivity analysis. Among these, the Levenberg–Marquardt back propagation neural network (LM-BPNN) model was identified as superior, achieving an accuracy of 96.85%, F1-score of 0.9113, and Kappa coefficient of 0.9417. Further validation in Wudong Coal Mine and Yvwu Coal Industry confirmed the model, achieving 100% accuracy. These findings underscore the LM-BPNN model’s potential as a viable tool for enhancing coal burst prevention strategies in coal mining sectors.

C@MOF composite material for rapid and efficient capture of gaseous iodine

In addressing the urgent challenge of mitigating gaseous iodine, a highly volatile and hazardous element in nuclear incidents, this study synthesized composite adsorbents with diverse C/Zn molar ratios using MOF and carbon powder. The saturated iodine adsorption at 75 °C exhibited the sequence: CZ-3 (1.15 g g−1) > CZ-2 (1.08 g g−1) > CZ-1 (0.93 g g−1) > C (0.79 g g−1), displaying 2.3–10.1 times increase in adsorption rate compared to carbon powder and Zn-MOF-2. At an elevated temperature of 95 °C, it achieved saturated adsorption of 1.29 g g−1(CZ-3). The adsorbed iodine primarily manifested in the forms of I2, I3-, and I5-. The dynamic maximum iodine adsorption capacity under specific conditions (75 °C, 300 ppm, 100 mL min−1, 0.5 MPa, 4 cm column length, and 6 mm diameter) reached 1077 mg g−1. Leveraging the Levenberg-Marquardt backpropagation neural network (LM-BPNN) algorithm, a superior fit was achieved. This research provides a theoretical foundation for the rapid and efficient treatment of gaseous iodine in nuclear scenarios.

A reliable neural network framework for the Zika system based reservoirs and human movement

The motive of current investigations provides the numerical solutions of the neuro computing solver based on the Levenberg-Marquardt backpropagation neural network approach (LMB) to solve the Zika virus system of reservoirs and human movement. The mathematical form of the human movement model is based on ten different classes, which makes the model nonlinear. The solution of this nonlinear mathematical model is presented by using the stochastic LMB neural network process. A dataset is designed based on the Adam solver in order to reduce the mean square error by dividing the data as training, endorsement, and testing with the use of 75%, 10% and 15%. Ten numbers of neurons have been taken by using the log-sigmoid transfer in the hidden layer for solving the human movement model. The precision of the proposed solver is authenticated by using the valuation of reference and proficient outcomes, while the reducible absolute error presents the correctness of the scheme. Moreover, the statistical performances based different proportional schemes have been used to authenticate the reliability of the scheme.

The significance of radiative heat and mass transfer through a vertical sheet with chemical reaction: Designing by artificial approach Levenberg-Marquardt

The present study examines the impact of incorporating soft computing algorithms during neural network training on the evaluation and prediction performance of artificial neural networks. The research centers on a magneto hydrodynamic flow model (RHMT-VSCR) that depicts the movement of rotating fluid along a vertical sheet in a permeable medium. Furthermore, the model considers the impact of heat source-sink interactions, thermal radiation, and reactive species in conjunction with the effects of Hall current on the enhancement of energy and solute profiles. Because the induced magnetic field has a negligible magnetic Reynolds number, it is disregarded. A set of governing nonlinear PDEs is converted to a system of ODEs by applying an appropriate postulate of similarity variables in order to analyze the system. The multilayer perceptron network utilizes a supervised Levenberg–Marquardt Backpropagation algorithm (LMLA-BPNN) to ascertain the ideal quantity of neurons to be included in the hidden layer of artificial neural networks intended for modeling purposes. The bvp4c numerical approach is utilized to establish a continuous neural network mapping, which produces datasets that are utilized for the purposes of authentication, training, and testing. The range i.e., 10−2−10−8 of absolute error of the reference and target data demonstrates the optimal accuracy performance of LMLA-BPNN networks. After acquiring knowledge of neural network mapping, it is applied to approximate solutions for a wide range of scenarios through the manipulation of physical constraints, including suction/injection quantities, magnetic parameter, and porosity parameter, which influence the characteristics of flow, energy, and concentration.To assess the precision of the proposed method, statistical graphs based on regression, mean squared error analysis graphs, and error histogram graphs are employed. The results of the research demonstrate that the Levenberg-Marquardt Backpropagation neural network mappings' derivation, convergence, authentication, and stability were effectively validated.

Machine learning prediction of biochar yield based on biomass characteristics

Slow pyrolysis is a widely used thermochemical pathway that can convert organic waste into biochar. We employed six machine learning models to predictively model 13 selected variables using pearson feature selection. Additionally, partial dependence analysis is used to reveal the deep relationship between feature variables. Both the gradient boosting decision tree and the Levenberg-Marquardt backpropagation neural network achieved training set R2 > 0.9 and testing set R2 > 0.8. But the other models displayed lower performance on the testing set, with R2 < 0.8. The partial dependence plot demonstrates that pyrolysis conditions have greater impact on biochar yield than biomass composition. Furthermore, the highest treatment temperature, being the sole consistently changing feature, can serve as a guiding factor for regulating biochar yield. This study highlights the immense potential of machine learning in experimental prediction, providing a scientific reference for reducing time and economic costs in pyrolysis experiments and process development.

Earthquake Forecasting Using Optimized Levenberg–marquardt Back-propagation Neural Network

In this study, an effective earthquake forecasting model is introduced using a hybrid metaheuristic machine learning (ML) algorithm with CUDA-enabled parallel processing. To improve the performance and accuracy of the model, a novel hybrid ML model is developed that utilizes parallel processing. The model consists of a Chaotic Chimp based African Vulture Optimization Algorithm (CCAVO) for feature selection and a Hybrid Levenberg-Marquardt Back-Propagation Neural Network (HLMt-BPNN) for prediction. The proposed model follows a four-step process: preprocessing the raw data to identify seismic indications, extracting features from the preprocessed data, using optimized ML algorithms to forecast the earthquake and its expected time, epicenter, and magnitude, and implementing the model using the Python platform. The model's performance is evaluated using various criteria, including accuracy, precision, recall, F-measure, specificity, false negative ratio, false positive ratio, negative prediction value, Matthew’s correlation coefficient, root mean square error, mean absolute error, and mean absolute percentage error. The proposed model achieved an accuracy of 98%, which is higher than the accuracy of existing earthquake prediction methods.

Lifetime Extension Approach Based on the Levenberg–Marquardt Neural Network and Power Routing of DC–DC Converters

The power conversion system based on the modular connection has widespread applications in various power electronic systems. To accurately estimate the state of health without recognizing the systematic mathematical model and to extend the lifetime, this paper proposes a lifetime extension approach based on the Levenberg-Marquardt back propagation neural network (LM-BPNN) and power routing of interleaved DC-DC boost conversion systems. The LM-BPNN model is constructed based on the voltage, current, and temperature data generated by the system. On the basis of the trained LM-BPNN, the real-time cumulated damage estimation of each power cell in the conversion system can be accomplished. Applying the power routing concept, the DC-DC boost conversion system allocates different power to the cells according to the cumulated damage of each cell, thereby delaying the failure of cells with higher cumulated damage. Numerical simulation results show that the proposed lifetime extension approach can extend the overall system lifetime. Furthermore, an experimental setup of the interleaved DC-DC boost conversion is constructed to verify the proposed approach, which is of great significance for predictive maintenance in the industrial system.

Two-Position Synthesis of the Four-Bar Planar Linkage Mechanisms Using Artificial Neural Networks

The four-bar linkage mechanism is a fundamental and widely recognized mechanism with diverse applications, including various vehicle components, rehabilitation robotics, and rotary and reciprocating engines. Traditional textbooks introduce graphical and analytical solutions for the kinematic synthesis problem of the four-bar mechanism in different positions, while research articles explore its applications in various fields. Recently, artificial neural network (ANN) methods have gained popularity across different research domains. Researchers have proposed different solution approaches using ANN algorithms for the inverse and forward kinematic analysis problems of these mechanisms. However, the specific use of ANN algorithms for solving the two-position kinematic synthesis problem of the four-bar planar linkage mechanism has not been explored yet. This study aims to address this gap by introducing a solution for the two-position kinematic synthesis problem of the four-bar planar linkage mechanism using an artificial neural network algorithm. The Levenberg-Marquardt backpropagation neural network algorithm is chosen due to its speed, combination of Gauss-Newton training algorithm and steepest descend method, and ability to provide stable convergence of the training error. The neural network algorithm is trained, validated, and tested using a total of 50 randomly split data sets. Additionally, an additional test is conducted using all 50 data sets to evaluate the performance of the trained neural network algorithm. The study presents and discusses the results of the artificial neural network algorithm solution.

Multimodal biometric recognition using fused handcrafted features

AbstractMultimodal biometric systems have been widely implemented in a variety of real-world scenarios due to their ability to overcome limitations associated with unimodal biometric systems. This paper is focused on the combination of the face, ear and gait in a unified multimodal biometric identification system using handcrafted features. These approaches provide robust and discriminative features to solve the biometric problem. In this research, speed up robust features and histogram of oriented gradients approaches have been used to extract features from face, ear and gait. The extracted features are optimized using genetic algorithm and classified using Levenberg-Marquardt backpropagation neural network. The system performance is evaluated on constrained and unconstrained dataset conditions.

Entropy optimization of non-Newtonian hybrid nanofluid EMHD flow by numerical and Levenberg–Marquardt backpropagation approach over a rotating disk

The goal of this study is to analyze the heat transfer characteristics of a Casson hybrid nanofluid flow system by incorporating EMHD (electromagnetohydrodynamic) and entropy production using the stochastic solver and Levenberg-Marquardt backpropagation neural networks. Self-similarity transformations are used to turn the mathematical model of a system of partial into a series of ordinary differential equations. The datasets are built through Runge–Kutta fourth order with a shooting approach, which aids in the generation of a continuous neural network mapping. To grasp the neural network mapping, the validation, training and testing processes are used to estimate the solutions of various physical constraints. For this hybrid model, the determination, convergence, verification and stability of Levenberg-Marquardt backpropagation neural network mappings are evaluated using regression-based statistical analysis, mean squared error and error histograms. To acquire high possibilities in drug delivery systems, hybrid nanofluids are regarded as blood-based nanofluids with dispersion of hybrid ( and ) nanoparticles. Due to this reason, this model might be beneficial for medication delivery systems, particularly if pharmaceuticals are injected into the bloodstream.

Darcy-Forchheimer hybrid nanofluid flow over the rotating Riga disk in the presence of chemical reaction: Artificial neural network approach

The aim of present study is to examine the augmentation of thermal energy transfer in hybrid nanofluid flow caused by a rotating Riga disk in the presence of thermal radiation and chemical reaction. The silver and aluminium oxide nanoparticles are used to examine the thermal effect of water base fluid. The Darcy-Forchheimer model is considered to endorse the inertial and porous media effects and makes the model more realistic from the physical scenario. Levenberg-Marquardt backpropagation algorithm is considered to analyze the hybrid nanofluid’s properties. Using scaling group transformations, the governing partial differential equations are transformed into a system of ordinary differential equations. Resulting ordinary differential equations are solved numerically by applying a suitable shooting technique by MATLAB. The results obtained for the governing differential equations have been incorporated into a dataset on which the neural network has been trained. The effects of physical parameters have been analyzed for velocity, temperature, and concentration profiles. The determination, designing, convergence, verification, and stability of the Levenberg-Marquardt backpropagation neural network algorithm are validated on the assessment of achieved accuracy through performance, fit, regression, and error histogram plots for the discussed hybrid nanofluid. It is observed that fluid velocity reduces for enhanced Darcy-Forchheimer number, magnetic parameters and boosted for enhanced modified Hartmann number. Temperature profile increases by increasing the Brownian motion and thermophoresis parameters.

Numerical solutions of the Wolbachia invasive model using Levenberg-Marquardt backpropagation neural network technique

The current study presents the numerical solutions of the Wolbachia invasive model (WIM) using the neural network Levenberg-Marquardt (NN-LM) backpropagation technique. The dynamics of the Wolbachia model is categorized into four classes, namely Wolbachia-uninfected aquatic mosquitoes (An∗), Wolbachia-uninfected adult female mosquitoes (Fn∗), Wolbachia-infected aquatic mosquitoes (Aw∗), and Wolbachia-infected adult female mosquitoes (Fw∗). A reference dataset for the proposed NN-LM technique is created by solving the Wolbachia model using the Runge-Kutta (RK) numerical method. The reference dataset is used for validation, training, and testing of the proposed NN-LM technique for three different cases. The obtained numerical results from the proposed neural network technique are compared with the results obtained from the RK method for accuracy, correctness, and efficiency of the designed methodology. The validation of the proposed solution methodology is checked through the mean square error (MSE), error histograms, error plots, regression plots, and fitness plots.

Levenberg-Marquardt backpropagation neural network procedures for the consumption of hard water-based kidney function

Abstract Water resources in Nusa Tenggara Timur have great concentrations based on magnesium and calcium ions thus being referred to as “hard water”. Prolonged hard water consumption has become a reason of kidney disfunction that can cause additional illnesses, like cerebrovascular pathologies and diabetes. Hence, it is crucial to comprehend how drinking hard water affects renal functions. The current study shows the kidney dysfunction model based on hard water consumption by applying the stochastic procedures of the Levenberg-Marquardt backpropagation neural networks (LMBNNs). The kidney dysfunction model of hard water consumption depends upon human components and water. Human dynamics is further divided into susceptible, infected and recovered, while water components are categorized into calcium and magnesium concentration. The log-sigmoid transfer function along with 20 hidden neurons is used to present the solutions of the model. Three cases of the model have been numerically stimulated and the correctness of the stochastic technique is perceived by using the comparison of proposed and reference Adam databased solutions along with the negligible absolute error. Training, validation and testing performances have been applied to reduce the values of the mean square error. Moreover, the statistical performances using the transition of state, error histograms and regression/correlation have been validated to authenticate the reliability of the scheme.

Assessment of transient thermal distribution in a moving porous plate with temperature-dependent internal heat generation using Levenberg–Marquardt backpropagation neural network

ABSTRACT In the present paper, the transient temperature distribution within a moving porous plate is scrutinized by considering the internal heat generation, convection and radiation condition. Furthermore, an effective artificial intelligence technique is proposed for discussing the transient thermal behavior in a moving plate by employing the Levenberg-Marquardt backpropagation network. Using suitable non-dimensional terms, the governing partial differential equation (PDE) is transformed into its non-dimensional form. The transformed equation is then solved using the finite difference method (FDM) and the obtained thermal values (datasets) are used as target values in the process of neural network mapping. This network uses the training, testing, and validation processes to determine the solution of several scenarios established at various process times. The behavior of dimensionless transient temperature profile has been inspected for diverse values of non-dimensional parameters such as heat generation number, radiative parameter, porosity parameter, and conductive parameter with the assistance of graphs. The significant outcomes elucidate that, the transient temperature profile of the porous plate increases with the change in time. Further, it enriches for rise in the magnitude of Peclet number and heat generation number while it drops for enhanced values of porosity parameter.

A predictive neuro-computing approach for micro-polar nanofluid flow along rotating disk in the presence of magnetic field and partial slip

&lt;abstract&gt; &lt;p&gt;The present study aims to design a Levenberg-Marquardt backpropagation neural network (LMB-NN) integrated numerical computing to investigate the problem of fluid mechanics governing the flow of magnetohydrodynamics micro-polar nanofluid flow over a rotating disk (MHD-MNRD) model along with the partial slip condition. In terms of PDEs, the basic system model MHD-MNRD is transformed into a system of non-linear ODEs by applying the similarity of transformations. For MHD-MNRD scenarios, the comparative dataset of the built LMB-NN procedure is formulated with the technique of Adams numerical by variation of micro-polar parameters, Brownian motion, Lewis number, magnetic parameter, velocity slip parameter and thermophoresis parameter. To compute the approximate solution for MHD-MNRD for various scenarios, validation, testing and training procedures are carried out in accordance to adjust the networks under the backpropagation procedure in terms of the mean square error (MSE). The efficiency of the designed LMB-NN methodology is highlighted by comparative study and performance analysis based on error histograms, MSE analysis, regression and correlation.&lt;/p&gt; &lt;/abstract&gt;

Numerical performances through artificial neural networks for solving the vector-borne disease with lifelong immunity

The current study is related to solve a nonlinear vector-borne disease with a lifelong immunity model (VDLIM) by designing a computational stochastic framework using the strength of artificial Levenberg-Marquardt backpropagation neural network (ALMBNN). The detail of the nonlinear VDLIM is provided along with its five classes. The numerical performances of the results have been presented using the ALMBNN by taking three different cases to solve the nonlinear VDLIM using the training, sample data, testing and authentication. The selection of the statics is selected as 80% for training, while the data for both testing and validations is applied 10%. The results of the nonlinear VDLIM are performed using the ALMBNN and the correctness of the scheme is observed to compare the results with the reference solutions. The calculated performance of the results to solve the nonlinear VDLIM is applied for the reduction of the mean square error. In order to check the competence, efficacy, exactness and reliability of the ALMBNN, the numerical investigations using the proportional procedures based on the MSE, correlation, regression and error histograms are presented.

Factors influencing indoor air pollution in buildings using PCA-LMBP neural network: A case study of a university campus

This study investigated indoor air quality (IAQ) and its association with building characteristics and environmental factors in eleven different campus buildings in Gainesville, Florida. Integrated indoor and outdoor sensor systems are built and installed to measure the levels of airborne particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), ozone (O3), relative humidity (RH), and temperature continuously in 10 min intervals for two weeks for each case building. Twenty building-related characteristics were collected through a walkthrough-based survey, HVAC historical data, and construction drawings. Through these data, a PCA-assisted Levenberg-Marquardt Backpropagation (LMBP) neural network model was developed for rapidly and accurately analyzing and predicting the interaction between IAQ and its affecting factors. Factors with significant contributions to indoor exposure were may mostly be determined by outdoor sources. This is evidenced by the strong associations that were found between indoor PM(2.5-10) and O3 values and their corresponding outdoor values and factors, including distance from the major traffic (DFMT), cracks occurring, outdoor temperature, and humidity. Indoor NO2 concentrations were affected by DFMT, indoor O3, indoor RH, number of air grilles, room volume, and window-to-wall ratio. Also, the comparison shows that the PCA-LMBP model outperforms the traditional BP-ANN and multi-linear regression methods. The average values of 1.34, 2.53, and 4.86 were obtained for the root-mean-square error (RMSE) of PCA-LMBP, BP-ANN, and MLR models, respectively. These results can be accordingly referred for the follow-up studies that analyze IAQ in similar building and environmental conditions.