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Journal articles on the topic 'Levenberg-Marquardt Back Propagation'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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Yang, Bo, Ning Li, Liang Lei, and Xue Wang. "BP Neural Network Fitting for Spectra of Blast Furnace Raceway." Applied Mechanics and Materials 55-57 (May 2011): 197–202. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.197.

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Three-layer BP neural network, particularly using Levenberg-Marquardt back-propagation with early stopping algorithm, is widely used in curve fitting, attributing to its fast speed and free from over-fitting. Hence, the trained network by Levenberg-Marquardt back-propagation was used for curve fitting of the radiation spectrum of blast furnace raceway. The results showed that Levenberg-Marquardt back-propagation with early stopping algorithm presented a better fitting ability. Additionally, the results of spectral fitting model showed that the blast furnace raceway had an effective radiation spectrum in the wavelength range from 420nm to 880nm, where the raceway could be considered as the gray body radiation.

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Zhang, Yu, Jiawen Zhang, Lin Luo, and Xiaorong Gao. "Optimization of LMBP high-speed railway wheel size prediction algorithm based on improved adaptive differential evolution algorithm." International Journal of Distributed Sensor Networks 15, no. 10 (2019): 155014771988134. http://dx.doi.org/10.1177/1550147719881348.

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It is beneficial for maintenance department to make maintenance strategy and reduce maintenance cost to forecast the hidden danger index value. Based on the analysis of the research status of wheel-to-life prediction at home and abroad and the repair of wheel-set wear and tear, this article designs and implements an adaptive differential evolution algorithm Levenberg–Marquardt back propagation wheel-set size prediction model. Aiming at the shortcomings of back propagation neural network, it is easy to fall into local extreme value. The back propagation algorithm is improved by Levenberg–Marquardt numerical optimization algorithm. Aiming at the shortcomings of back propagation neural network algorithm for randomly initializing connection weights and thresholds to fall into local extreme value, the differential evolution algorithm is used to optimize the initial connection weights and thresholds between the layers of the neural network. In order to speed up the search of the optimal initial weights and thresholds of the differential evolution algorithm Levenberg–Marquardt back propagation neural network, the initial values are further optimized, and an adaptive differential evolution algorithm Levenberg–Marquardt back propagation wheel-set size prediction model is designed and implemented. Compared with the proposed combine adaptive differential evolution algorithm with LMBP optimization (ADE-LMBP) is effective and significantly improves the prediction accuracy.

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Garkani-Nejad, Zahra, and Behzad Ahmadi-Roudi. "Investigating the role of weight update functions in developing artificial neural network modeling of retention times of furan and phenol derivatives." Canadian Journal of Chemistry 91, no. 4 (2013): 255–62. http://dx.doi.org/10.1139/cjc-2012-0372.

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A quantitative structure−retention relationship study has been carried out on the retention times of 63 furan and phenol derivatives using artificial neural networks (ANNs). First, a large number of descriptors were calculated using HyperChem, Mopac, and Dragon softwares. Then, a suitable number of these descriptors were selected using a multiple linear regression technique. This paper focuses on investigating the role of weight update functions in developing ANNs. Therefore, selected descriptors were used as inputs for ANNs with six different weight update functions including the Levenberg−Marquardt back-propagation network, scaled conjugate gradient back-propagation network, conjugate gradient back-propagation with Powell−Beale restarts network, one-step secant back-propagation network, resilient back-propagation network, and gradient descent with momentum back-propagation network. Comparison of the results indicates that the Levenberg−Marquardt back-propagation network has better predictive power than the other methods.

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Cigizoglu, H. Kerem, and Özgür Kişi. "Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data." Hydrology Research 36, no. 1 (2005): 49–64. http://dx.doi.org/10.2166/nh.2005.0005.

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Flow forecasting performance by artificial neural networks (ANNs) is generally considered to be dependent on the data length. In this study k-fold partitioning, a statistical method, was employed in the ANN training stage. The method was found useful in the case of using the conventional feed-forward back propagation algorithm. It was shown that with a data period much shorter than the whole training duration similar flow prediction performance could be obtained. Prediction performance and convergence velocity comparison between three different back propagation algorithms, Levenberg–Marquardt, conjugate gradient and gradient descent was the next concern of the study and the Levenberg–Marquardt technique was found advantageous thanks to its shorter training duration and more satisfactory performance criteria.

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Yang, Bo, Ning Li, Xue Wang, and Liang Lei. "Spectra Modeling of Blast Furnace Raceway by Neural Network." Applied Mechanics and Materials 55-57 (May 2011): 245–50. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.245.

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Neural network with Levenberg-Marquardt back-propagation training is widely used in curve fitting, according to its fast speed and free from over-fitting. In order to solve the issue on local minimum that may be found in Levenberg-Marquardt back-propagation with early stopping, and to get optimum number of hidden neurons, the least mean test errors algorithm was used in repeatedly training the three-layer feed-forward network with variable structure. Furthermore, the trained network was used for curve fitting of the radiation spectrum of blast furnace raceway. The results on spectra modeling of blast furnace raceway showed that this algorithm presented a better fitting ability, characterized by the reservation of the details of the original spectra and better generalization ability.

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Baruch, Ieroham, and Edmundo P. Reynaud. "Recurrent Neural Adaptive Control of Nonlinear Oscillatory Systems Using a Complex-valued Levenberg-Marquardt Learning Algorithm." Information Technologies and Control 13, no. 1-2 (2015): 10–24. http://dx.doi.org/10.1515/itc-2016-0007.

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Abstract In this work, a Recursive Levenberg-Marquardt learning algorithm in the complex domain is developed and applied in the training of two adaptive control schemes composed by Complex-Valued Recurrent Neural Networks. Furthermore, we apply the identification and both control schemes for a particular case of nonlinear, oscillatory mechanical plant to validate the performance of the adaptive neural controller and the learning algorithm. The comparative simulation results show the better performance of the newly proposed Complex-Valued Recursive Levenberg-Marquardt learning algorithm over the gradient-based recursive Back-propagation one.

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P., Anil Kumar, and Anuradha B. "Reflectivity Parameter Extraction from RADAR Images Using Back Propagation Algorithms." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 2795–803. https://doi.org/10.11591/ijece.v8i5.pp2795-2803.

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Pattern recognition has been acknowledged as one of the promising research areas and it has drawn the awareness among many researchers since its existence at the beginning of the nineties. Multilayer Neural networks are used in pattern Recognition and classification based on the features derived from the input patterns. The Reflectivity information extracted from the Doppler Weather Radar (DWR) image helps in identifying the convective cloud type which has a strong relation to the precipitation rate. The reflectivity information is rooted in the DWR image with the help of colors and color bar is provided to distinguish among different reflectivity information. Artificial Neural network predicts the color based on the maximum likelihood estimation problem. This paper presents a best possible backpropagation algorithm for color identification in DWR images by comparing various backpropagation algorithms such as Levenberg-Marquardt, Conjugate gradient, and Resilient back propagation etc.,. Pattern recognition using Neural networks presents better results compared to standard distance measures. It is observed that Levenberg-Marquardt backpropagation algorithm yields a regression value of 99% approximately and accuracy of 98%.

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Kollam, Manoj, and Ajay Joshi. "Earthquake Forecasting Using Optimized Levenberg–marquardt Back-propagation Neural Network." WSEAS TRANSACTIONS ON COMPUTERS 22 (August 3, 2023): 90–97. http://dx.doi.org/10.37394/23205.2023.22.11.

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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.

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Salmasi, Mehrshad, and Homayoun Mahdavi-Nasab. "Evaluating the Performance of Training Algorithms in Active Noise Control Using MLP Neural Network." Advanced Materials Research 468-471 (February 2012): 1613–17. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.1613.

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Passive methods are costly and ineffective in noise reduction at low frequencies. Active noise control has been suggested because of these problems. Active noise control (ANC) is based on the destructive interference between the noise source waves and a controlled secondary source. In this paper, various training algorithms are compared in active cancellation of modeled sound noise using MLP neural network. Colored noise signals are used as a model of sound noise instead of noise signals from databases. An MLP neural network with different architectures is used in simulation procedure. The effect of number of neurons on the convergence speed of various training algorithms is investigated in this paper. Levenberg-Marquardt (LM), scaled conjugate gradient (SCG), BFGS quasi-Newton (BFG), resilient back-propagation (RP) and variable learning rate back-propagation (GDX) are used for training the network. Simulation results show that Levenberg-Marquardt (LM) and scaled conjugate gradient (SCG) are the fastest training algorithms.

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Nawi, Nazri Mohd, Abdullah Khan, and M. Z. Rehman. "CSLM: Levenberg Marquardt based Back Propagation Algorithm Optimized with Cuckoo Search." Journal of ICT Research and Applications 7, no. 2 (2013): 103–16. http://dx.doi.org/10.5614/itbj.ict.res.appl.2013.7.2.1.

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Kadnár, Milan, Peter Káčer, Marta Harničárová, et al. "Comparison of Linear Regression and Artificial Neural Network Models for the Dimensional Control of the Welded Stamped Steel Arms." Machines 11, no. 3 (2023): 376. http://dx.doi.org/10.3390/machines11030376.

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The production of parts by pressing and subsequent welding is commonly used in the automotive industry. The disadvantage of this method of production is that inaccuracies arising during pressing significantly affect the final dimension of the part. However, this can be corrected by the choice of the technological parameters of the following operation—welding. Suitably designed parameters make it possible to partially eliminate inaccuracies arising during pressing and thus increase the overall applicability of this technology. The paper is focused on the upper arm geometry of a car produced in this manner. There have been two neural networks proposed in which the optimal welding parameters are determined based on the stamped dimensions and the desired final dimensions. The Levenberg–Marquardt back-propagation algorithm and the Bayesian regularised back-propagation algorithm were used as the learning algorithm for ANNs in multi-layer feed-forward networks. The outputs obtained from the neural networks were compared with a linear prediction model based on a on the design of experiment methodology. The mean absolute percentage error of the linear regression model on the entire dataset was 3 × 10−3%. A neural network with Levenberg–Marquardt back-propagation learning algorithm had a mean absolute percentage error of 4 × 10−3. Similarly, a neural network with a Bayesian regularised back-propagation learning algorithm had a mean absolute percentage error of 3 × 10−3%.

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Chithambaram, T., and K. Perumal. "Comparative Study: Artificial Neural Networks Training Functions for Brain Tumor Segmentation for MRI Images." Journal of Computational and Theoretical Nanoscience 17, no. 4 (2020): 1831–38. http://dx.doi.org/10.1166/jctn.2020.8448.

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Brain tumor detection from medical images is essential to diagnose earlier and to take decision in treatment planning. Magnetic Resonance Images (MRI) is frequently preferred for detecting brain tumors by the physicians. This paper analyses various Artificial Neural Networks (ANN) training functions for brain tumor segmentation such as Levenberg-Marquardt (LM), Quasi Newton back propagation (QN), Bayesian regularization (BR), Resilient back propagation algorithm (RP) and Scaled conjugate gradient back propagation (SCG). The training algorithms were employed in different sized network for segmentation. The results were carefully analyzed and measured using Dice similarity, sensitivity, specificity and accuracy measures.

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Kozlova, L. E., and E. V. Bolovin. "Building the Structure and the Neuroemulator Angular Velocity's Learning Algorithm Selection of the Electric Drive of TVR-IM Type." Applied Mechanics and Materials 792 (September 2015): 44–50. http://dx.doi.org/10.4028/www.scientific.net/amm.792.44.

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Today, one of the most common ways to control smooth starting and stopping of the induction motors are soft-start system. To ensure such control method the use of closed-speed asynchronous electric drive of TVR-IM type is required. Using real speed sensors is undesirable due to a number of inconveniences exploitation of the drive. The use of the observer based on a neural network is more convenient than the use of the real sensors. Its advantages are robustness, high generalizing properties, lack of requirements to the motor parameters, the relative ease of creation. This article presents the research and selection of the best learning algorithm of the neuroemulator angular velocity of the electric drive of TVR-IM type. There were investigated such learning algorithms as gradient descent back propagation, gradient descent with momentum back propagation, algorithm of Levenberg – Marquardt, scaled conjugate gradient back propagation (SCG). The input parameters of the neuroemulator were the pre treatment signals from the real sensors the stator current and the stator voltage and their delay, as well as a feedback signal from the estimated speed with delay. A comparative analysis of learning algorithms was performed on a simulation model of asynchronous electric drive implemented in software MATLAB Simulink, when the electric drive was running in dynamic mode. The simulation results demonstrate that the best method of learning is algorithm of Levenberg – Marquardt.

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Nawi, Nazri Mohd, Abdullah Khan, and M. Z. Rehman. "A New Levenberg Marquardt based Back Propagation Algorithm Trained with Cuckoo Search." Procedia Technology 11 (2013): 18–23. http://dx.doi.org/10.1016/j.protcy.2013.12.157.

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Tawfiq, Luma N. M., and Othman M. Salih. "Using Feed Forward Neural Network to Solve Eigenvalue Problems." Conference Papers in Science 2014 (March 31, 2014): 1–8. http://dx.doi.org/10.1155/2014/906376.

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The aim of this paper is to presents a parallel processor technique for solving eigenvalue problem for ordinary differential equations using artificial neural networks. The proposed network is trained by back propagation with different training algorithms quasi-Newton, Levenberg-Marquardt, and Bayesian Regulation. The next objective of this paper was to compare the performance of aforementioned algorithms with regard to predicting ability.

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Younis, Younis M., Salman H. Abbas, Farqad T. Najim, Firas Hashim Kamar, and Gheorghe Nechifor. "Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups." Revista de Chimie 71, no. 6 (2020): 66–74. http://dx.doi.org/10.37358/rc.20.6.8171.

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A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

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Abdullah, Normah, Muhammad Harith Anuar, Zulkifli Mohd Nopiah, et al. "Modelling of Two Continuous Stirred Tank Heat Exchangers in Series Using Neural Network." International Journal of Engineering & Technology 8, no. 1.2 (2019): 250–55. http://dx.doi.org/10.14419/ijet.v8i1.2.24910.

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This paper presents the application of artificial neural networks (ANN) in modeling of two continuous stirred tank heat echangers in series (2CSTHEs), which is a complex non-linear process. Non-linear models of the 2CSTHEs system were developed using ANN because ofÂ ANN ability to model complex non-linear processes without requiring any explicit knowledge about input-output relationship. The ANN architectureÂ is based on the multilayer feed forward network and it is trained using the back-propagation algorithms. Three types of back-propagation algorithms are used in the study, namely, Levenberg-Marquardt, BFGS quasi-Newton, and conjugate gradient with Polak-RibiÃ©re updates. Two dynamic models of the system are developed: ANN model for CSTHE 1and 2. Results from the study showed that the 2CSTHEs model trained using Levenberg-Marquardt algorithm produced the best predictive performance of the system behaviour. The results confirmed that ANN can be used in the modeling of the heat exchanger 2CSTHEs, and the model obtained can predict the outputs of the system process with very high accuracy. This proves that ANN modelling method can produce accurate system models that can simulate and predict the behaviour of complex non-linear processes.Â

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Huang, Xinyi, Hao Cao, and Bingjing Jia. "Optimization of Levenberg Marquardt Algorithm Applied to Nonlinear Systems." Processes 11, no. 6 (2023): 1794. http://dx.doi.org/10.3390/pr11061794.

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As science and technology advance, industrial manufacturing processes get more complicated. Back Propagation Neural Network (BPNN) convergence is comparatively slower for processing nonlinear systems. The nonlinear system used in this study to evaluate the optimization of BPNN based on the LM algorithm proved the algorithm’s efficacy through a MATLAB simulation analysis. This paper examined the application impact of the enhanced approach using the Continuous stirred tank reactor (CSTR) control system as an example. The study’s findings demonstrate that the LM optimization algorithm’s identification error exceeds 10-5. The research’s suggested control approach for reactant concentration CA in CSTR systems provides a better tracking effect and a stronger anti-interference capacity. Compared to the PI control method, the overall control effect is superior. As a result, the optimization model for nonlinear systems has a greatly improved processing accuracy. With some data support for the accuracy study of neural network models and the application of nonlinear systems, the suggested LM-BP optimization algorithm is evidently more appropriate for nonlinear systems.

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Zhang, Jun, Yu Tian, Zongjin Ren, Qingbing Chang, and Zhenyuan Jia. "The calibration of force offset for rocket engine based on deep belief network." Measurement and Control 51, no. 5-6 (2018): 172–81. http://dx.doi.org/10.1177/0020294018776442.

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Background Force offset is an important movement and control parameter in rocket motor development process, and its accurate measurement is a vital guarantee of rocket motor reliable operation, so there is an essential significance to achieve accurate force offset calibration. Methods A novel force offset nonlinear calibration method is proposed based on deep belief network. Experimental platform is established and force offset calibration test is completed. Because the Levenberg -Marquardt process has the advantage of both Newton method and gradient descent method, test data are trained with Levenberg -Marquardt, decreasing nonlinear mapping convergence errors and realizing nonlinear calibration of force offset. Results and Conclusions Training results show that the mean deviation rate of force offset after nonlinear calibration is less than 2.7%, better than the back-propagation neural network and least squares method, verifying the reasonableness and practicality of nonlinear compensation calibration method and effectively improving force offset calibration accuracy.

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Ding, Shuo, Xiao Heng Chang, and Qing Hui Wu. "A Study on Approximation Performances of General Regression Neural Network." Applied Mechanics and Materials 441 (December 2013): 713–16. http://dx.doi.org/10.4028/www.scientific.net/amm.441.713.

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In order to study the approximation performance of general regression neural networks, the structure and algorithm of general regression neural networks are first introduced. Then general regression neural networks and back propagation neural networks improved by Levenberg-Marquardt algorithm are established through programming using MATLAB language. A certain nonlinear function is taken as an example to be approximated by the two kinds of neural networks. The simulation results indicate that compared with back propagation neural networks, general regression neural networks has better approximation precision and faster convergence speed, which means it has much better approximation ability than back propagation neural networks. Therefore, for more complex function approximation, general regression neural networks is recommended. It can reduce the complexity of neural networks and it is also easier to design.

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Li, Shuo, Song Li, Haifeng Zhao, and Yuan An. "Design and implementation of state-of-charge estimation based on back-propagation neural network for smart uninterruptible power system." International Journal of Distributed Sensor Networks 15, no. 12 (2019): 155014771989452. http://dx.doi.org/10.1177/1550147719894526.

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In this article, a method for estimating the state of charge of lithium battery based on back-propagation neural network is proposed and implemented for uninterruptible power system. First, back-propagation neural network model is established with voltage, temperature, and charge–discharge current as input parameters, and state of charge of lithium battery as output parameter. Then, the back-propagation neural network is trained by Levenberg–Marquardt algorithm and gradient descent method; and the state of charge of batteries in uninterruptible power system is estimated by the trained back-propagation neural network. Finally, we build a state-of-charge estimation test platform and connect it to host computer by Ethernet. The performance of state-of-charge estimation based on back-propagation neural network is tested by connecting to uninterruptible power system and compared with the ampere-hour counting method and the actual test data. The results show that the state-of-charge estimation based on back-propagation neural network can achieve high accuracy in estimating state of charge of uninterruptible power system and can reduce the error accumulation caused in long-term operation.

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Tawfiq, Luma N. M., and Ashraf A. T. Hussein. "Design Feed Forward Neural Network to Solve Singular Boundary Value Problems." ISRN Applied Mathematics 2013 (August 28, 2013): 1–7. http://dx.doi.org/10.1155/2013/650467.

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The aim of this paper is to design feed forward neural network for solving second-order singular boundary value problems in ordinary differential equations. The neural networks use the principle of back propagation with different training algorithms such as quasi-Newton, Levenberg-Marquardt, and Bayesian Regulation. Two examples are considered to show that effectiveness of using the network techniques for solving this type of equations. The convergence properties of the technique and accuracy of the interpolation technique are considered.

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Kusumoputro, Benyamin, Rozandi Prarizky, Wahidin Wahab, Dede Sutarya, and Li Na. "Assesment of Quality Classification of Green Pellets for Nuclear Power Plants Using Improved Levenberg-Marquardt Algorithm." Advanced Materials Research 608-609 (December 2012): 825–34. http://dx.doi.org/10.4028/www.scientific.net/amr.608-609.825.

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Cylindrical uranium dioxide pellets, which are the main components for nuclear fuel elements in Light Water Reactor, should have a high density profile, uniform shape and quality for the safety used as a reactor fuel component. The quality of green pellets is conventionally monitored through a laboratory measurement of the physical pellets characteristics followed by a graphical chart classification technique. However, this conventional classification method shows some drawbacks, such as the difficulties on its usage, low accuracy and time consuming, and does not have the ability to adress the non-linearity and the complexity of the relationship between the pellet’s quality variables and the pellett’s quality. In this paper, an Improved Levenberg-Marquard based neural networks is used to classify the quality process of the green pellets. Robustness of this learning algorithm is evaluated by comparing its recognition rate to that of the conventional Back Propagation neural learning algorithm. Results show that the Improved Levenberg-Marquard algorithm outperformed the Back Propagation learning algorthm for various percentage of training/testing paradigm, showing that this system could be applied effectively for classification of pellet quality.

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Yadav, Arvind, Premkumar Chithaluru, Aman Singh, et al. "An Enhanced Feed-Forward Back Propagation Levenberg–Marquardt Algorithm for Suspended Sediment Yield Modeling." Water 14, no. 22 (2022): 3714. http://dx.doi.org/10.3390/w14223714.

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Rivers are dynamic geological agents on the earth which transport the weathered materials of the continent to the sea. Estimation of suspended sediment yield (SSY) is essential for management, planning, and designing in any river basin system. Estimation of SSY is critical due to its complex nonlinear processes, which are not captured by conventional regression methods. Rainfall, temperature, water discharge, SSY, rock type, relief, and catchment area data of 11 gauging stations were utilized to develop robust artificial intelligence (AI), similar to an artificial-neural-network (ANN)-based model for SSY prediction. The developed highly generalized global single ANN model using a large amount of data was applied at individual gauging stations for SSY prediction in the Mahanadi River basin, which is one of India’s largest peninsular rivers. It appeared that the proposed ANN model had the lowest root-mean-squared error (0.0089) and mean absolute error (0.0029) along with the highest coefficient of correlation (0.867) values among all comparative models (sediment rating curve and multiple linear regression). The ANN provided the best accuracy at Tikarapara among all stations. The ANN model was the most suitable substitute over other comparative models for SSY prediction. It was also noticed that the developed ANN model using the combined data of eleven stations performed better at Tikarapara than the other ANN which was developed using data from Tikarapara only. These approaches are suggested for SSY prediction in river basin systems due to their ease of implementation and better performance.

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Ardıçlıoğlu, Mehmet, Özgür Kişi, and Tefaruk Haktanır. "Suspended sediment prediction using two different feed-forward back-propagation algorithms." Canadian Journal of Civil Engineering 34, no. 1 (2007): 120–25. http://dx.doi.org/10.1139/l06-111.

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IIn this paper the capability of two different feed-forward back-propagation neural network algorithms, namely Levenberg-Marquardt and gradient-descent, in solving complex nonlinear problems is utilized for suspended sediment prediction. The monthly streamflow and suspended sediment data from two stations, Palu and Çayağzi, in the Firat Basin in Turkey are used as case studies. The first part of the study involves the prediction of sediment data for the two stations. The second part of the study focuses on the prediction of the downstream station sediment data using upstream data. The effect of the periodicity on model performance is also investigated in each application.Key words: suspended sediment, neural networks, multilinear regression, prediction.

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Sun, Jinjuan, Zhiheng Ma, and Jianhui Tian. "Prediction of frosting process on cold wall surface based on artificial neural network with back propagation algorithm." Thermal Science, no. 00 (2023): 55. http://dx.doi.org/10.2298/tsci221126055s.

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The artificial neural network with back propagation algorithm is a multi-layer feed-forward neural network, which is suitable to study unsteady frost formation with multiple factors. The back propagation artificial neural network algorithm is used to study frost layer growth on cold flat surface, where four feature variables including temperature of cold flat surface, the velocity, relative humidity and temperature of air are adopted. The frost growth experiment generates the database, which is good for training frost growth due to its fast speed and high precision based on Levenberg-Marquardt learning rule. The establishment of neural network model in this paper can quickly and accurately predict the frost layer height on cold flat surface of different control variables, which is helpful for the implementation of defrosting.

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Rohit, Jha* Ravindra Pratap Narwaria. "ANALYSIS OF DIRECTIVITY AND BANDWIDTH OF COAXIAL FEED SQUARE MICROSTRIP PATCH ANTENNA USING ARTIFICIAL NEURAL NETWORK." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 12 (2016): 845–52. https://doi.org/10.5281/zenodo.221116.

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In this paper the use of artificial neural network for the estimation of Directivity and Bandwidth of coaxial feed square shaped microstrip patch antenna is presented. Multilayer Perceptron Feedforward Back Propagation Network (MLPFFBP-ANN) with Levenberg-Marquardt (L-M) training algorithms has been used in order to implement the neural network model. The results obtained from the Artificial Neural Network Model are equated with the results obtained from the Computer Simulation Technology (CST) Studio Software, and the results show satisfactory agreement, and also it is noted that the neural network model is not trained ve

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Idemudia, O; Ehiorobo J.O; Izinyon O.C and Ilaboya I.R. "Application of Artificial Neural Network (ANN) for the Modelling and Prediction of Ikpoba River Flow Data." Journal of Science and Technology Research 5, no. 4 (2024): 169–81. https://doi.org/10.5281/zenodo.10529880.

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<em>This study focuses on evaluating the effectiveness of artificial neural network (ANN), specifically employing the Levenberg Marquardt Back Propagation algorithm, for streamflow prediction in Ikpoba River, Benin City. Hydrological data from September 2022 to March 2023, along with historical data (2010-2015), were used to establish a cubic polynomial relationship between gage height and river discharge. The ANN model, with 10 hidden neurons, demonstrated a strong performance with a mean square error of 0.000303, surpassing the target error of 0.01. The study highlights the significance of accurate streamflow prediction models, especially in mitigating floods and optimizing reservoir management. </em>

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Du, Yi-Chun, and Alphin Stephanus. "Levenberg-Marquardt Neural Network Algorithm for Degree of Arteriovenous Fistula Stenosis Classification Using a Dual Optical Photoplethysmography Sensor." Sensors 18, no. 7 (2018): 2322. http://dx.doi.org/10.3390/s18072322.

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This paper proposes a noninvasive dual optical photoplethysmography (PPG) sensor to classify the degree of arteriovenous fistula (AVF) stenosis in hemodialysis (HD) patients. Dual PPG measurement node (DPMN) becomes the primary tool in this work for detecting abnormal narrowing vessel simultaneously in multi-beds monitoring patients. The mean and variance of Rising Slope (RS) and Falling Slope (FS) values between before and after HD treatment was used as the major features to classify AVF stenosis. Multilayer perceptron neural networks (MLPN) training algorithms are implemented for this analysis, which are the Levenberg-Marquardt, Scaled Conjugate Gradient, and Resilient Back-propagation, to identify the degree of HD patient stenosis. Eleven patients were recruited with mean age of 77 ± 10.8 years for analysis. The experimental results indicated that the variance of RS in the HD hand between before and after treatment was significant difference statistically to stenosis (p < 0.05). Levenberg-Marquardt algorithm (LMA) was significantly outperforms the other training algorithm. The classification accuracy and precision reached 94.82% and 92.22% respectively, thus this technique has a potential contribution to the early identification of stenosis for a medical diagnostic support system.

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Heidari, Mohammad, and Hadi Homaei. "Estimation of Acceleration Amplitude of Vehicle by Back Propagation Neural Networks." Advances in Acoustics and Vibration 2013 (June 4, 2013): 1–7. http://dx.doi.org/10.1155/2013/614025.

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This paper investigates the variation of vertical vibrations of vehicles using a neural network (NN). The NN is a back propagation NN, which is employed to predict the amplitude of acceleration for different road conditions such as concrete, waved stone block paved, and country roads. In this paper, four supervised functions, namely, newff, newcf, newelm, and newfftd, have been used for modeling the vehicle vibrations. The networks have four inputs of velocity (), damping ratio (), natural frequency of vehicle shock absorber (), and road condition (R.C) as the independent variables and one output of acceleration amplitude (AA). Numerical data, employed for training the networks and capabilities of the models in predicting the vehicle vibrations, have been verified. Some training algorithms are used for creating the network. The results show that the Levenberg-Marquardt training algorithm and newelm function are better than other training algorithms and functions. This method is conceptually straightforward, and it is also applicable to other type vehicles for practical purposes.

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Wang, Zhi Wei, Yi Peng, Zhong He Zhang, Wei Cao, and Peng Li. "Study on Non Energy Saving Status Detection of Groundwater Heat Pump System Using Artificial Neural Network Method." Advanced Materials Research 443-444 (January 2012): 325–32. http://dx.doi.org/10.4028/www.scientific.net/amr.443-444.325.

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Presents two types of characteristic data: basic characteristic parameters and index characteristic parameters for non energy saving status detection (NESSD) of groundwater heat pump (GWHP) system, establishes the relationship database between characteristic data and fault factors of NESSD. For three kinds of improving back propagation (BP) algorithms: Variable Learning Rate (VLR) BP algorithm, Scaled Conjugate Gradient (SCG) BP algorithm, and Levenberg-Marquardt (LM) BP algorithm, these various algorithms’ comparative study had been conducted on the GWHP system’s NESSD. The optimal algorithm among them is determined and the GWHP system’s NESSD as cases studies can be carried out based on the most suitable BP algorithm.

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Kumar, P. Anil, and B. Anuradha. "Reflectivity Parameter Extraction from RADAR Images Using Back Propagation Algorithm." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 2795. http://dx.doi.org/10.11591/ijece.v8i5.pp2795-2803.

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<p class="Abstract">Pattern recognition has been acknowledged as one of the promising research areas and it has drawn the awareness among many researchers since its existence at the beginning of the nineties. Multilayer Neural networks are used in pattern Recognition and classification based on the features derived from the input patterns. The Reflectivity information extracted from the Doppler Weather Radar (DWR) image helps in identifying the convective cloud type which has a strong relation to the precipitation rate. The reflectivity information is rooted in the DWR image with the help of colors and color bar is provided to distinguish among different reflectivity information. Artificial Neural network predicts the color based on the maximum likelihood estimation problem. This paper presents a best possible backpropagation algorithm for color identification in DWR images by comparing various backpropagation algorithms such as LevenbergMarquardt, Conjugate gradient, and Resilient back propagation etc.,. Pattern recognition using Neural networks presents better results compared to standard distance measures. It is observed that Levenberg-Marquardt backpropagation algorithm yields a regression value of 99% approximately and accuracy of 98%</p>

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Ayyıldız, Mustafa, and Kerim Çetinkaya. "Predictive modeling of geometric shapes of different objects using image processing and an artificial neural network." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 231, no. 6 (2016): 1206–16. http://dx.doi.org/10.1177/0954408916659310.

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In this study, an artificial neural network model was developed to predict the geometric shapes of different objects using image processing. These objects with various sizes and shapes (circle, square, triangle, and rectangle) were used for the experimental process. In order to extract the features of these geometric shapes, morphological features, including the area, perimeter, compactness, elongation, rectangularity, and roundness, were applied. For the artificial neural network modeling, the standard back-propagation algorithm was found to be the optimum choice for training the model. In the building of the network structure, five different learning algorithms were used: the Levenberg–Marquardt, the quasi-Newton back propagation, the scaled conjugate gradient, the resilient back propagation, and the conjugate gradient back propagation. The best result was obtained by 6-5-1 network architectures with single hidden layers for the geometric shapes. After artificial neural network training, the correlation coefficients ( R2) of the geometric shape values for training and testing data were very close to 1. Similarly, the root-mean-square error and mean error percentage values for the training and testing data were less than 0.9% and 0.004%, respectively. These results demonstrated that the artificial neural network is an admissible model for the estimation of geometric shapes using image processing.

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Miao, Xin Ying, Jin Kui Chu, Jing Qiao, and Ling Han Zhang. "Predicting Seepage of Earth Dams Using Neural Network and Genetic Algorithm." Advanced Materials Research 403-408 (November 2011): 3081–85. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3081.

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Measurements of seepage are fundamental for earth dam surveillance. However, it is difficult to establish an effective and practical dam seepage prediction model due to the nonlinearity between seepage and its influencing factors. Genetic Algorithm for Levenberg-Marquardt(GA-LM), a new neural network(NN) model has been developed for predicting the seepage of an earth dam in China using 381 databases of field data (of which 366 in 2008 were used for training and 15 in 2009 for testing). Genetic algorithm(GA) is an ecological system algorithm, which was adopted to optimize the NN structure. Levenberg-Marquardt (LM) algorithm was originally designed to serve as an intermediate optimization algorithm between the Gauss-Newton(GN) method and the gradient descent algorithm, which was used to train NN. The predicted seepage values using GA-LM model are in good agreement with the field data. It is demonstrated here that the model is capable of predicting the seepage of earth dams accurately. The performance of GA-LM has been compared with that of conventional Back-Propagation(BP) algorithm and LM algorithm with trial-and-error approach. The comparison indicates that the GA-LM model can offer stronger and better performance than conventional NNs when used as a quick interpolation and extrapolation tool.

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Al-batah, Mohammad Subhi, Mutasem Sh Alkhasawneh, Lea Tien Tay, Umi Kalthum Ngah, Habibah Hj Lateh, and Nor Ashidi Mat Isa. "Landslide Occurrence Prediction Using Trainable Cascade Forward Network and Multilayer Perceptron." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/512158.

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Landslides are one of the dangerous natural phenomena that hinder the development in Penang Island, Malaysia. Therefore, finding the reliable method to predict the occurrence of landslides is still the research of interest. In this paper, two models of artificial neural network, namely, Multilayer Perceptron (MLP) and Cascade Forward Neural Network (CFNN), are introduced to predict the landslide hazard map of Penang Island. These two models were tested and compared using eleven machine learning algorithms, that is, Levenberg Marquardt, Broyden Fletcher Goldfarb, Resilient Back Propagation, Scaled Conjugate Gradient, Conjugate Gradient with Beale, Conjugate Gradient with Fletcher Reeves updates, Conjugate Gradient with Polakribiere updates, One Step Secant, Gradient Descent, Gradient Descent with Momentum and Adaptive Learning Rate, and Gradient Descent with Momentum algorithm. Often, the performance of the landslide prediction depends on the input factors beside the prediction method. In this research work, 14 input factors were used. The prediction accuracies of networks were verified using the Area under the Curve method for the Receiver Operating Characteristics. The results indicated that the best prediction accuracy of 82.89% was achieved using the CFNN network with the Levenberg Marquardt learning algorithm for the training data set and 81.62% for the testing data set.

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Beigi, Mohsen, Mehdi Torki-Harchegani, and Mahmood Mahmoodi-Eshkaftaki. "Prediction of paddy drying kinetics: A comparative study between mathematical and artificial neural network modelling." Chemical Industry and Chemical Engineering Quarterly 23, no. 2 (2017): 251–58. http://dx.doi.org/10.2298/ciceq160524039b.

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The present study aimed at investigation of deep bed drying of rough rice kernels at various thin layers at different drying air temperatures and flow rates. A comparative study was performed between mathematical thin layer models and artificial neural networks to estimate the drying curves of rough rice. The suitability of nine mathematical models in simulating the drying kinetics was examined and the Midilli model was determined as the best approach for describing drying curves. Different feed forward-back propagation artificial neural networks were examined to predict the moisture content variations of the grains. The ANN with 4-18-18-1 topology, transfer function of hyperbolic tangent sigmoid and a Levenberg-Marquardt back propagation training algorithm provided the best results with the maximum correlation coefficient and the minimum mean square error values. Furthermore, it was revealed that ANN modeling had better performance in prediction of drying curves with lower root mean square error values.

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Hassan, Musavir, Muheet Ahmad Butt, and Majid Zaman Baba. "Logistic Regression Versus Neural Networks: The Best Accuracy in Prediction of Diabetes Disease." Asian Journal of Computer Science and Technology 6, no. 2 (2017): 33–42. http://dx.doi.org/10.51983/ajcst-2017.6.2.1782.

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To derive actionable insights from vast amount of data in an intelligent fashion some techniques are used called machine learning techniques. These techniques support for predicting disease with correct case of training and testing. To classify the medical data logistic regression and artificial neural networks are the models to be selected. Today in world a major health problem is Diabetes Mellitus for which many classification algorithms have been applied for its diagnoses and treatment. To detect diabetes disease in early stage it needs greatest support of machine learning, since it cannot be cured and also brings great complication to our health system. In this paper, we establish a general framework for explaining the functioning of Artificial Neural Networks (ANNs) in binomial classification and implement and evaluate the variants of Back propagation algorithm (Standard Back Propagation, Resilient – Back propagation, Variable Learning Rate, Powell-Beale Conjugate Gradient, Levenberg Marquardt, Quasi-Newton Algorithm and Scaled Conjugate Gradient) using Pima Indians Diabetes Data set from UCI repository of machine learning databases. We also compare Artificial Neural Networks (ANNs) with one of the conventional techniques, namely logistic regression (LR) to predict diabetic disease decisions.

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Katip, Aslıhan, and Asifa Anwar. "Modeling the Influence of Climate Change on the Water Quality of Doğancı Dam in Bursa, Turkey, Using Artificial Neural Networks." Water 17, no. 5 (2025): 728. https://doi.org/10.3390/w17050728.

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Population growth, industrialization, excessive energy consumption, and deforestation have led to climate change and affected water resources like dams intended for public drinking water. Meteorological parameters could be used to understand these effects better to anticipate the water quality of the dam. Artificial neural networks (ANNs) are favored in hydrology due to their accuracy and robustness. This study modeled climatic effects on the water quality of Doğancı dam using a feed-forward neural network with one input, one hidden, and one output layer. Three models were tested using various combinations of meteorological data as input and Doğancı dam’s water quality data as output. Model success was determined by the mean squared error and correlation coefficient (R) between the observed and predicted data. Resilient back-propagation and Levenberg–Marquardt were tested for each model to find an appropriate training algorithm. The model with the least error (1.12–1.68) and highest R value (0.93–0.99) used three meteorological inputs (air temperature, global solar radiation, and solar intensity), six water quality parameters of Doğancı dam as output (water temperature, pH, dissolved oxygen, manganese, arsenic, and iron concentrations), and ten hidden nodes. The two training algorithms employed in this study did not differ statistically (p > 0.05). However, the Levenberg–Marquardt training approach demonstrated a slight advantage over the resilient back-propagation algorithm by achieving reduced error and higher correlation in most of the models tested in this study. Also, better convergence and faster training with a lesser gradient value were noted for the LM algorithm. It was concluded that ANNs could predict a dam’s water quality using meteorological data, making it a useful tool for climatological water quality management and contributing to sustainable water resource planning.

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Alasl, M. Kashefi, M. Khosravi, M. Hosseini, G. R. Pazuki, and R. Nezakati Esmail Zadeh. "Measurement and mathematical modelling of nutrient level and water quality parameters." Water Science and Technology 66, no. 9 (2012): 1962–67. http://dx.doi.org/10.2166/wst.2012.333.

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Physico-chemical water quality parameters and nutrient levels such as water temperature, turbidity, saturated oxygen, dissolved oxygen, pH, chlorophyll-a, salinity, conductivity, total nitrogen and total phosphorus, were measured from April to September 2011 in the Karaj dam area, Iran. Total nitrogen in water was modelled using an artificial neural network system. In the proposed system, water temperature, depth, saturated oxygen, dissolved oxygen, pH, chlorophyll-a, salinity, turbidity and conductivity were considered as input data, and the total nitrogen in water was considered as output. The weights and biases for various systems were obtained by the quick propagation, batch back propagation, incremental back propagation, genetic and Levenberg–Marquardt algorithms. The proposed system uses 144 experimental data points; 70% of the experimental data are randomly selected for training the network and 30% of the data are used for testing. The best network topology was obtained as (9-5-1) using the quick propagation method with tangent transform function. The average absolute deviation percentages (AAD%) are 2.329 and 2.301 for training and testing processes, respectively. It is emphasized that the results of the artificial neural network (ANN) model are compatible with the experimental data.

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Ni, Yuan Ping, and Hui Ye. "Studies of an Improved LMBP Model for Predicting Potential Distribution of Insects." Advanced Materials Research 459 (January 2012): 594–98. http://dx.doi.org/10.4028/www.scientific.net/amr.459.594.

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An improved LMBP algorithm model was presented based on analysing the shortcomings of back propagation (BP)neural network and discussing the idea of Levenberg-Marquardt (LM)algorithm. The simulating data proved that the improved LMBP model was able to overcome its local minimum and increase the converging speed in comparison with BP algorithm. Meanwhile this model was applied to predicting the potential distribution of insects. Here we take an example of oriental fruit fly Bactrocera dorsalis. The experimental results show that the algorithm model has a capacity of learning and can solve the forecasting problem in potential distribution of oriental fruit fly. The algorithm model is better than other traditional method and is very useful and efficient in practice

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Adda, Asma, Salah Bezari, Mohamed Salmi, et al. "Investigation of the Efficiency of Small-Scale NF/RO Seawater Desalination by Using Artificial Neural Network Modeling." International Journal of Design & Nature and Ecodynamics 16, no. 3 (2021): 293–99. http://dx.doi.org/10.18280/ijdne.160307.

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An attempt is conducted in this paper to develop an artificial neural network (ANN) model for predicting the efficiency of small-scale NF/RO seawater desalination, then applied to the simulation of permeate flow rate and water recovery. A feed-forward back-propagation neural network with the Levenberg-Marquardt learning algorithm is considered. The performance of ANN compared to the multiple linear regression (MLR) is based on the calculated value of the coefficient of determination (R2). For ANN, R2 permeate flow rate was 0.997, and R2 permeate water recovery was 0.999, and for MLR, R2 permeate flow rate was 0.508, and R2 permeate water recovery was 0.713. It was observed that ANN performed better than the MLR.

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Ramana, Maram Venkata, Indra Putra Almanar, Mohd Zulkifly Abdullah, Zaidi Mohd Ripin, and K. N. Seetaramu. "Design and Optimization of Piezoelectric Fans for Cooling of Microelectronic Devices." Journal of Microelectronics and Electronic Packaging 4, no. 3 (2007): 121–29. http://dx.doi.org/10.4071/1551-4897-4.3.121.

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Static, modal, and dynamic finite element analyses have been performed for cantilever piezoelectric fans to understand their mechanical behavior and to aid in design-optimization studies. Three-dimensional analysis was carried out by using the finite element analysis software package, ANSYS. Various parameters—like length, thickness, location of the piezoelectric metal layer, temperature, damping ratio, and electric field—have been considered. The effects of these parameters on the vibration characteristics and performance are investigated and consolidated through artificial neural networks. Feed-forward single hidden layer perceptron neural networks and Levenberg–Marquardt back-propagation (LMBP) algorithms are used to train the neural network. Optimal geometrical dimensions for maximum performance are then obtained using genetic algorithms.

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Lin, Hsuan Liang, and Chang Pin Chou. "Optimizing the RSW Process Quality of Auto-Body via the Taguchi Method and a Neural-Genetic Approach." Advanced Materials Research 97-101 (March 2010): 3899–904. http://dx.doi.org/10.4028/www.scientific.net/amr.97-101.3899.

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This paper applies an integrated approach using the Taguchi method, neural network (NN) and genetic algorithm (GA) to optimize the tensile-shear strength of resistance spot welding (RSW) specimens in automotive industry. The proposed approach consists of two stages. First stage executes initial optimization via Taguchi method to construct a database for the NN. In second stage, a NN with Levenberg-Marquardt back-propagation (LMBP) algorithm is used to provide the nonlinear relationship between factors and the response. Then, a GA is applied to obtain the optimal factor settings. The experimental results showed that the tensile-shear strength of the optimal welding parameter via the proposed approach is better than apply Taguchi method only.

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Sitharthan, R., K. R. Devabalaji, and Arun Jees. "An Levenberg–Marquardt trained feed-forward back-propagation based intelligent pitch angle controller for wind generation system." Renewable Energy Focus 22-23 (December 2017): 24–32. http://dx.doi.org/10.1016/j.ref.2017.10.003.

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Blom, Philip S. "What else can we do with auxiliary parameters in ray tracing? How about back projection for localization?" Journal of the Acoustical Society of America 155, no. 3_Supplement (2024): A72. http://dx.doi.org/10.1121/10.0026846.

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Auxiliary parameters describing variations in ray path geometry with respect to initial launch angles have been leveraged in computing the Jacobian determinant needed to solve the transport equation as well as to build a Levenberg–Marquardt algorithm for identification of source-receiver paths (termed eigenrays) in a 3D inhomogeneous moving atmosphere. Building on these applications, recent investigations have demonstrated that these parameters can be computed along back projected ray paths from infrasonic detections to improve Bayesian localization capabilities. An overview of the auxiliary parameters as introduced in previous work will be provided along with a summary of current localization development. Example applications of the method will be presented and compared with existing Bayesian infrasonic localization methods using more general propagation models.

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Baiju, V., and C. Muraleedharan. "Artificial neural network modelling of adsorbent bed in a solar adsorption refrigeration system." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 227, no. 2 (2012): 346–58. http://dx.doi.org/10.1177/0954406212448606.

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This article analyses the adsorbent bed in an adsorption refrigeration system. After establishing the similarity to the compression process in a vapour compression system, thermodynamic analysis of the adsorbent bed in vapour adsorption system is carried out for evaluating the performance index, exergy destruction, uptake efficiency and exergetic efficiency of the adsorbent bed in a typical solar adsorption refrigeration system. This article also presents isothermal and isobaric modelling of methanol on highly porous activated carbon. The experimental data have been fitted with Dubinin–Astakhov and Dubinin–Radushkevitch equations. The isosteric heat of adsorption is also extracted from the present experimental data. The use of artificial neural network model is proposed to predict the performance of the adsorbent bed used. The back propagation algorithm with three different variants namely scaled conjugate gradient, Pola–Ribiere conjugate gradient and Levenberg–Marquardt and logistic sigmoid transfer function are used, so that the best approach could be found. After training, it is found that Levenberg–Marquardt algorithm with 14 neurons is the most suitable for modelling, the adsorbent bed in a solar adsorption refrigeration system. The artificial neural network predictions of performance parameters agrees well with experimental values with correlation coefficient ( R2) values close to 1 and maximum percentage of error less than 5%. The root mean square and covariance values are also found to be within the acceptable limits.

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Kumari, G. Vimala, G. Sasibhushana Rao, and B. Prabhakara Rao. "New Artificial Neural Network Models for Bio Medical Image Compression." International Journal of Applied Metaheuristic Computing 10, no. 4 (2019): 91–111. http://dx.doi.org/10.4018/ijamc.2019100106.

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This article presents an image compression method using feed-forward back-propagation neural networks (NNs). Marked progress has been made in the area of image compression in the last decade. Image compression removing redundant information in image data is a solution for storage and data transmission problems for huge amounts of data. NNs offer the potential for providing a novel solution to the problem of image compression by its ability to generate an internal data representation. A comparison among various feed-forward back-propagation training algorithms was presented with different compression ratios and different block sizes. The learning methods, the Levenberg Marquardt (LM) algorithm and the Gradient Descent (GD) have been used to perform the training of the network architecture and finally, the performance is evaluated in terms of MSE and PSNR using medical images. The decompressed results obtained using these two algorithms are computed in terms of PSNR and MSE along with performance plots and regression plots from which it can be observed that the LM algorithm gives more accurate results than the GD algorithm.

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Ganesh Wakte. "Computational Forecasting of Power Prices Using Artificial Neural Networks." Advances in Nonlinear Variational Inequalities 27, no. 3 (2024): 33–43. http://dx.doi.org/10.52783/anvi.v27.1356.

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In the restructured power markets, the primary responsibility is setting the price of electricity. Thus, it has become increasingly important to accurately and precisely forecast power prices. An Artificial Neural Network (ANN) that was developed specifically for temporary price prediction in restructured electricity markets is presented in this paper. An input level, two hidden layers, and output layer comprise the four levels of the suggested ANN model, which is a perceptron neural network. Instead of using traditional back propagation for ANN training, using Levenberg-Marquardt retrogression (LMBP) methodology is used to accelerate convergence. The performance and efficacy of the suggested ANN model may be shown by training it on the Ontario power market. MATLAB is used to train the model.

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S J, Elliot, Anireh V.I.E, and Nwiabu N.D. "A Predictive Model for Cloud Computing Security in Banking Sector Using Levenberg Marquardt Back Propagation with Cuckoo Search." International Journal of Computer Science and Engineering 7, no. 2 (2020): 42–47. http://dx.doi.org/10.14445/23488387/ijcse-v7i2p105.

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Vijayalakshmi, P., S. Sivasankar, and G. Karthikeyan. "Simulation of wind turbine system using field oriented control and Levenberg-Marquardt Back Propagation (LMBP) neural network model." Biomedical Signal Processing and Control 92 (June 2024): 105961. http://dx.doi.org/10.1016/j.bspc.2024.105961.

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