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Academic literature on the topic 'Levenberg-Marquardt training algorithm'

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Published: 3 June 2025

Last updated: 19 July 2025

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Journal articles on the topic "Levenberg-Marquardt training algorithm"

Bilski, Jarosław, Jacek Smoląg, Bartosz Kowalczyk, Konrad Grzanek, and Ivan Izonin. "Fast Computational Approach to the Levenberg-Marquardt Algorithm for Training Feedforward Neural Networks." Journal of Artificial Intelligence and Soft Computing Research 13, no. 2 (2023): 45–61. http://dx.doi.org/10.2478/jaiscr-2023-0006.

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Abstract This paper presents a parallel approach to the Levenberg-Marquardt algorithm (LM). The use of the Levenberg-Marquardt algorithm to train neural networks is associated with significant computational complexity, and thus computation time. As a result, when the neural network has a big number of weights, the algorithm becomes practically ineffective. This article presents a new parallel approach to the computations in Levenberg-Marquardt neural network learning algorithm. The proposed solution is based on vector instructions to effectively reduce the high computational time of this algor

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Mustafidah, Hindayati, and Suwarsito Suwarsito. "Performance of Levenberg-Marquardt Algorithm in Backpropagation Network Based on the Number of Neurons in Hidden Layers and Learning Rate." JUITA: Jurnal Informatika 8, no. 1 (2020): 29. http://dx.doi.org/10.30595/juita.v8i1.7150.

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One of the supervised learning paradigms in artificial neural networks (ANN) that are in great developed is the backpropagation model. Backpropagation is a perceptron learning algorithm with many layers to change weights connected to neurons in hidden layers. The performance of the algorithm is influenced by several network parameters including the number of neurons in the input layer, the maximum epoch used, learning rate (lr) value, the hidden layer configuration, and the resulting error (MSE). Some of the tests conducted in previous studies obtained information that the Levenberg-Marquardt

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Guo, Huang, Bao Ru Han, and Guo Fang Zhang. "Analog Circuit Fault Diagnosis Based on Levenberg-Marquardt Learning Algorithm." Applied Mechanics and Materials 380-384 (August 2013): 979–82. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.979.

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This paper presents a fault diagnosis method of BP neural network based on Levenberg-Marquardt learning algorithm. First, the use of principal component analysis to reduce the dimension of the fault sample reduced BP neural network input variables. Then use the Levenberg-Marquardt learning algorithm to adjust the network weights. Levenberg-Marquardt learning algorithm is combination of the Gauss - Newton algorithm and steepest descent algorithm. It has Gauss - Newton algorithm of local convergence and gradient descent algorithm of the global characteristic. So it has higher convergence speed,

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Lera, G., and M. Pinzolas. "Neighborhood based Levenberg-Marquardt algorithm for neural network training." IEEE Transactions on Neural Networks 13, no. 5 (2002): 1200–1203. http://dx.doi.org/10.1109/tnn.2002.1031951.

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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,

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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 th

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Sajindra, Hirushan, Thilina Abekoon, Eranga M. Wimalasiri, Darshan Mehta, and Upaka Rathnayake. "An Artificial Neural Network for Predicting Groundnut Yield Using Climatic Data." AgriEngineering 5, no. 4 (2023): 1713–36. http://dx.doi.org/10.3390/agriengineering5040106.

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Groundnut, being a widely consumed oily seed with significant health benefits and appealing sensory profiles, is extensively cultivated in tropical regions worldwide. However, the yield is substantially impacted by the changing climate. Therefore, predicting stressed groundnut yield based on climatic factors is desirable. This research focuses on predicting groundnut yield based on several combinations of climatic factors using artificial neural networks and three training algorithms. The Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient algorithms were evaluated for

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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 o

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Altaye, Aschenaki, Istvan Farkas, and Piroska Víg. "Impacts of Artificial Neural Network Training Algorithms on the Accuracy of PV System Voltage and Current Predictions." European Journal of Energy Research 5, no. 3 (2025): 1–6. https://doi.org/10.24018/ejenergy.2025.5.3.161.

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This study highlights the importance of selecting the appropriate Artificial Neural Network (ANN) training algorithm-based accuracy of prediction capacities in photovoltaic (PV) systems. Accurate PV system performance prediction, particularly output voltage and current, is essential for optimising energy generation and ensuring grid stability. This study evaluates the impact of three ANN training algorithms Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) on the prediction of PV voltage and current. The algorithms were tested using solar radiation and

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Arthur, C. K., V. A. Temeng, and Y. Y. Ziggah. "Performance Evaluation of Training Algorithms in Backpropagation Neural Network Approach to Blast-Induced Ground Vibration Prediction." Ghana Mining Journal 20, no. 1 (2020): 20–33. http://dx.doi.org/10.4314/gm.v20i1.3.

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Abstract Backpropagation Neural Network (BPNN) is an artificial intelligence technique that has seen several applications in many fields of science and engineering. It is well-known that, the critical task in developing an effective and accurate BPNN model depends on an appropriate training algorithm, transfer function, number of hidden layers and number of hidden neurons. Despite the numerous contributing factors for the development of a BPNN model, training algorithm is key in achieving optimum BPNN model performance. This study is focused on evaluating and comparing the performance of

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Dissertations / Theses on the topic "Levenberg-Marquardt training algorithm"

"Applying Levenberg-Marquardt algorithm with block-diagonal Hessian approximation to recurrent neural network training." 1999. http://library.cuhk.edu.hk/record=b5896325.

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by Chi-cheong Szeto. Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. Includes bibliographical references (leaves 162-165). Abstracts in English and Chinese. Abstract --- p.i Acknowledgment --- p.ii Table of Contents --- p.iii Chapter Chapter 1 --- Introduction --- p.1 Chapter 1.1 --- Time series prediction --- p.1 Chapter 1.2 --- Forecasting models --- p.1 Chapter 1.2.1 --- Networks using time delays --- p.2 Chapter 1.2.1.1 --- Model description --- p.2 Chapter 1.2.1.2 --- Limitation --- p.3 Chapter 1.2.2 --- Networks using context units

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Book chapters on the topic "Levenberg-Marquardt training algorithm"

Iplikci, Serdar, Batuhan Bilgi, Ali Menemen, and Bedri Bahtiyar. "A Novel Modification on the Levenberg-Marquardt Algorithm for Avoiding Overfitting in Neural Network Training." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30484-3_17.

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Dias, Fernando Morgado, Ana Antunes, José Vieira, and Alexandre Manuel Mota. "On-line Training of Neural Networks: A Sliding Window Approach for the Levenberg-Marquardt Algorithm." In Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499305_59.

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Priya, Anu, and Shruti Garg. "A Comparison of Prediction Capabilities of Bayesian Regularization and Levenberg–Marquardt Training Algorithms for Cryptocurrencies." In Smart Intelligent Computing and Applications. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9282-5_62.

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Tabbussum, Ruhhee, and Abdul Qayoom Dar. "Analysis of Bayesian Regularization and Levenberg–Marquardt Training Algorithms of the Feedforward Neural Network Model for the Flow Prediction in an Alluvial Himalayan River." In Cybernetics, Cognition and Machine Learning Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1632-0_5.

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Lahmiri, Salim. "On Simulation Performance of Feedforward and NARX Networks Under Different Numerical Training Algorithms." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8823-0.ch005.

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This chapter focuses on comparing the forecasting ability of the backpropagation neural network (BPNN) and the nonlinear autoregressive moving average with exogenous inputs (NARX) network trained with different algorithms; namely the quasi-Newton (Broyden-Fletcher-Goldfarb-Shanno, BFGS), conjugate gradient (Fletcher-Reeves update, Polak-Ribiére update, Powell-Beale restart), and Levenberg-Marquardt algorithm. Three synthetic signals are generated to conduct experiments. The simulation results showed that in general the NARX which is a dynamic system outperforms the popular BPNN. In addition, conjugate gradient algorithms provide better prediction accuracy than the Levenberg-Marquardt algorithm widely used in the literature in modeling exponential signal. However, the LM performed the best when used for forecasting the Moroccan and South African stock price indices under both the BPNN and NARX systems.

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Das, Raja, and Mohan Kumar Pradhan. "Artificial Neural Network Training Algorithms in Modeling of Radial Overcut in EDM." In Soft Computing Techniques and Applications in Mechanical Engineering. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3035-0.ch006.

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This chapter describes with the comparison of the most used back propagations training algorithms neural networks, mainly Levenberg-Marquardt, conjugate gradient and Resilient back propagation are discussed. In the present study, using radial overcut prediction as illustrations, comparisons are made based on the effectiveness and efficiency of three training algorithms on the networks. Electrical Discharge Machining (EDM), the most traditional non-traditional manufacturing procedures, is growing attraction, due to its not requiring cutting tools and permits machining of hard, brittle, thin and complex geometry. Hence it is very popular in the field of modern manufacturing industries such as aerospace, surgical components, nuclear industries. But, these industries surface finish has the almost importance. Based on the study and test results, although the Levenberg-Marquardt has been found to be faster and having improved performance than other algorithms in training, the Resilient back propagation algorithm has the best accuracy in testing period.

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Das, Raja, and Mohan Kumar Pradhan. "Artificial Neural Network Training Algorithms in Modeling of Radial Overcut in EDM." In Research Anthology on Artificial Neural Network Applications. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-2408-7.ch015.

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Kumari, G. Vimala, G. Sasibhushana Rao, and B. Prabhakara Rao. "New Artificial Neural Network Models for Bio Medical Image Compression." In Research Anthology on Artificial Neural Network Applications. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-2408-7.ch042.

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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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Istomin, S. G., K. I. Domanov, A. P. Shatokhin, and I. N. Denisov. "SELECTION AND JUSTIFICATION OF THE METHOD AND ALGORITHM OF MACHINE LEARNING FOR SOLVING THE PROBLEMS OF ENERGY-OPTIMAL LOCOMOTIVE MOVEMENT." In Intelligent Transportation Systems. FSBEO HPE Moscow State University of Railway Engineering (MIIT), 2025. https://doi.org/10.30932/9785002587582-2025-205-210.

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The article discusses the most common methods of machine learning and artificial intelligence that can be used to solve problems of energy-optimal locomotive movement. It has been established that in terms of the ratio of the accuracy of the model, characterized by the total mean square error and the determination coefficient R2, and the time spent on training the model, it is preferable to use the Levenberg-Marquardt method. However, it is worth noting that faster but less accurate are the gradient descent methods, which can also be used to solve problems of constructing energy-optimal locomotive movement modes.

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Selvanambi, Ramani, and Jaisankar N. "Healthcare." In Research Anthology on Medical Informatics in Breast and Cervical Cancer. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7136-4.ch010.

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Quality analysis of the treatment of cancer has been an objective of e-health services for quite some time. The objective is to predict the stage of breast cancer by using diverse input parameters. Breast cancer is one of the main causes of death in women when compared to other tumors. The classification of breast cancer information can be profitable to anticipate diseases or track the hereditary of tumors. For classification, an artificial neural network (ANN) structure was carried out. In the structure, nine training algorithms are used and the proposed is the Levenberg-Marquardt algorithm. For optimizing the hidden layer and neuron, three optimization techniques are used. In the result, the best approval execution is anticipated and the diverse execution evaluation estimation for three optimization algorithms is researched. The correlation execution diagram for an accuracy of 95%, a sensitivity of 98%, and a specificity of 89% of a social spider optimization (SSO) algorithm are shown.

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Conference papers on the topic "Levenberg-Marquardt training algorithm"

Kim, Cheol-Taek, Ju-Jang Lee, and Hyejin Kim. "Variable Projection Method and Levenberg-Marquardt Algorithm for Neural Network Training." In IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics. IEEE, 2006. http://dx.doi.org/10.1109/iecon.2006.347644.

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Reynaldi, Arnold, Samuel Lukas, and Helena Margaretha. "Backpropagation and Levenberg-Marquardt Algorithm for Training Finite Element Neural Network." In 2012 European Modelling Symposium (EMS). IEEE, 2012. http://dx.doi.org/10.1109/ems.2012.56.

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Ampazis, N., and S. J. Perantonis. "Levenberg-Marquardt algorithm with adaptive momentum for the efficient training of feedforward networks." In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. IEEE, 2000. http://dx.doi.org/10.1109/ijcnn.2000.857825.

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Kaminski, Marcin. "Neural estimators of two-mass system optimized using the Levenberg-Marquardt training and genetic algorithm." In 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR). IEEE, 2016. http://dx.doi.org/10.1109/mmar.2016.7575197.

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Nguyen, Lien B., Anh V. Nguyen, Sai Ho Ling, and Hung T. Nguyen. "Combining genetic algorithm and Levenberg-Marquardt algorithm in training neural network for hypoglycemia detection using EEG signals." In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013. http://dx.doi.org/10.1109/embc.2013.6610766.

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Khanesar, Mojtaba Ahmadieh, Erdal Kayacan, Mohammad Teshnehlab, and Okyay Kaynak. "Levenberg marquardt algorithm for the training of type-2 fuzzy neuro systems with a novel type-2 fuzzy membership function." In 2011 IEEE Symposium On Advances In Type-2 Fuzzy Logic Systems - Part Of 17273 - 2011 Ssci. IEEE, 2011. http://dx.doi.org/10.1109/t2fuzz.2011.5949558.

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He, Hong-Wei, and Zao-Jian Zou. "Black-Box Modeling of Ship Maneuvering Motion Using System Identification Method Based on BP Neural Network." In ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/omae2020-18069.

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Abstract This paper deals with the black-box modeling of 3-DOF nonlinear maneuvering motion of surface ships by using system identification method based on BP neural network. A Mariner Class vessel is taken as the study object. The time series used in training and testing the network is the simulated data of a series of maneuvers, which is obtained by numerically solving the Abkowitz model using fourth-order Runge-Kutta method. A three-layer neural network is built to solve this multivariable regression fitting problem, and only one network model is trained to predict various ship maneuvers. T

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Kinser, Robert Eric, Deborah A. Furey, and Othon K. Rediniotis. "Calibration Neural Network for a Novel Omni-Directional Velocity Probe: PROBENET." In ASME 1996 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/imece1996-0948.

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Abstract In this paper, the development of an adaptive neural network for the calibration of novel velocity measurement instrumentation is presented. The backpropagation-based algorithm, PROBENET, was initially developed for the calibration of multi-hole pressure probes, although at this point it has evolved to a generic-application code. The code offers distinct advantages over commercial packages (such as the Matlab Neural Network Toolkit, Demuth and Beale, 1994) in terms of maximum allowable network size, training convergence rates, flexibility in network architecture design and network opt

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A´lvarez del Castillo, A., E. Santoyo, and O. Garci´a-Valladares. "Development of a New Void Fraction Correlation for Modeling Two-Phase Flow in Producing Geothermal Wells Using Artificial Neural Networks." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-40444.

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An artificial neural network (ANN) was used to develop a new empirical correlation to estimate void fractions for modeling two-phase flows in geothermal wells. Flowing pressure, wellbore diameter, steam quality, fluid density and viscosity, and Reynolds numbers were used as input data. An explicit relationship among the input data was obtained from an ANN model. A computational architecture based on, the Levenberg-Marquardt optimization algorithm, the hyperbolic tangent sigmoid transfer-function, and the linear transfer-function, was designed. A geothermal database containing thirty-two data s

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Guo, Chang, Ming Gao, Peixin Dong, Yuetao Shi, and Fengzhong Sun. "Prediction Model of Flow-Induced Noise in Large-Scale Centrifugal Pumps Based on BP Neural Network." In ASME 2017 Power Conference Joint With ICOPE-17 collocated with the ASME 2017 11th International Conference on Energy Sustainability, the ASME 2017 15th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2017 Nuclear Forum. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/power-icope2017-3280.

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As one kind of serious environmental problems, flow-induced noise in centrifugal pumps pollutes the working circumstance and deteriorates the performance of pumps, meanwhile, it always changes drastically under various working conditions. Consequently, it is extremely significant to predict flow-induced noise of centrifugal pumps under various working conditions with a practical mathematical model. In this paper, a three-layer back propagation (BP) neural network model is established and the number of input, hidden and output layer node is set as 3, 6 and 1, respectively. To be specific, the f

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Reports on the topic "Levenberg-Marquardt training algorithm"

Arhin, Stephen, Babin Manandhar, Hamdiat Baba Adam, and Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.1943.

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Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automati

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