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

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

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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Katip, Aslıhan, and Asifa Anwar. "Simulating the Impacts of Climate Change on the Hydrology of Doğancı Dam in Bursa, Turkey, Using Feed-Forward Neural Networks." Sustainability 17, no. 14 (2025): 6273. https://doi.org/10.3390/su17146273.

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Climate change continues to pose significant challenges to global water security, with dams being particularly vulnerable to hydrological cycle alterations. This study investigated the climate-based impact on the hydrology of the Doğancı dam, located in Bursa, Turkey, using feed-forward neural networks (FNNs). The modeling used meteorological parameters as inputs. The employed FNN comprised one input, hidden, and output layer. The efficacy of the models was evaluated by comparing the correlation coefficients (R), mean squared errors (MSE), and mean absolute percentage errors (MAPE). Furthermor

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Irianto, Ade Gilang Hendra, and Endah Sudarmilah. "Pengaruh Variasi Jumlah Neuron dalam Hidden Layer Algoritma Pelatihan Levenberg-Marquardt Jaringan Backpropagation: A Systematic Literature Review." JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI 10, no. 2 (2025): 180. https://doi.org/10.36722/sst.v10i2.3788.

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<p><strong>This analysis is done to determine and is a consideration for future research related to different types of problem solving by using the training algorithm of Backpropagation network. This study uses 4 steps selections in filter articles that will be used in literary studies, namely 1) Identification 2) Screening 3) Eligibility and 4) Included. The number of items filtered in this study is 73 articles. The article was filtered through the identification phase with a total of 205 articles, then in the screening process by assimilating the title and summary, then the eligi

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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 analys

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Alabbawi, Ali Abbawi Mohammed, Ibrahim Ismael Alnaib, Omar Sharaf Al-Deen Yehya Al-Yozbaky, and Karam Khairullah Mohammed. "Faults detection, location, and classification of the elements in the power system using intelligent algorithm." Bulletin of Electrical Engineering and Informatics 12, no. 2 (2023): 597–607. http://dx.doi.org/10.11591/eei.v12i2.4456.

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This study proposes an intelligent protection relay design that uses artificial neural networks to secure electrical parts in power infrastructure from different faults. Electrical transformer and transmission lines are protected using intelligent differential and distance relay, respectively. Faults are categorized, and their locations are pinpointed using three-phase current values and zero-current characteristics to differentiate between non-earth and ground faults. The optimal aspects of the artificial neural network were chosen for optimal results with the least possible error. Levenberg-

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Ahmad, Hafiz Waqar, Jeong Ho Hwang, Kamran Javed, Umer Masood Chaudry, and Dong Ho Bae. "Probabilistic Fatigue Life Prediction of Dissimilar Material Weld Using Accelerated Life Method and Neural Network Approach." Computation 7, no. 1 (2019): 10. http://dx.doi.org/10.3390/computation7010010.

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Welding alloy 617 with other metals and alloys has been receiving significant attention in the last few years. It is considered to be the benchmark for the development of economical hybrid structures to be used in different engineering applications. The differences in the physical and metallurgical properties of dissimilar materials to be welded usually result in weaker structures. Fatigue failure is one of the most common failure modes of dissimilar material welded structures. In this study, fatigue life prediction of dissimilar material weld was evaluated by the accelerated life method and a

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Bajeh, Amos Orenyi, Muftau Oluwatosin Wasiu, and Fatima Enehezei Usman-Hamza. "Performance Evaluation of Optimised Backpropagation Algorithms for Yorùbá Character Feature Extraction and Recognition." DIU Journal of Science & Technology 14, no. 1 (2024): 1–8. https://doi.org/10.5281/zenodo.13770953.

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Character recognition has been an important area of research in the last few decades. It is basically divided into two major types namely online and offline (handwritten) character recognitions. Characters with tonal marks (diacritics) such as Yorùbá characters (orthography) had posed more challenges than their counterparts with no tonal marks and as a result require some optimization methods to improve the recognition rate and reduce the error rate. This study evaluated the performance of four optimized backpropagation algorithms, Levenberg-Marquardt, Quasi-Newton BFGS, Resilien

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Suliman, Azizah, and Batyrkhan Omarov. "Early Stopping Criteria for Levenberg-Marquardt Based Neural Network Training Optimization." International Journal of Engineering & Technology 7, no. 4.36 (2018): 1194. http://dx.doi.org/10.14419/ijet.v7i4.36.25382.

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In this research we train a direct distributed neural network using Levenberg-Marquardt algorithm. In order to prevent overtraining, we proposed correctly recognized image percentage based on early stop condition and conduct the experiments with different stop thresholds for image classification problem. Experiment results show that the best early stop condition is 93% and other increase in stop threshold can lead to decrease in the quality of the neural network. The correct choice of early stop condition can prevent overtraining which led to the training of a neural network with considerable

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Gao, Qing, Qin He Zhang, Shu Peng Su, Jian Hua Zhang, and Rong Yu Ge. "Prediction Models and Generalization Performance Study in Electrical Discharge Machining." Applied Mechanics and Materials 10-12 (December 2007): 677–81. http://dx.doi.org/10.4028/www.scientific.net/amm.10-12.677.

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In the past decade, artificial neural network(ANN) has been applied in Electrical discharge machining(EDM). However, most of them only discuss parameter prediction or optimization result, few tell how to improve generalization performance. In this study, machining process models have been established based on different training algorithms of ANN, namely Levenberg-Marquardt algorithm (LM), Resilient algorithm (RP), Scaled Conjugate Gradient algorithm (SCG) and Quasi-Newton algorithm(BFGS). All models have been trained by same experimental data, checked by another group data, their generalizatio

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Al Kautsar, Hafizh. "Model Fourier Untuk Prediksi Harga Saham Astrazeneca Menggunakan Algoritma Levenberg-Marquardt." JURNAL TIKA 6, no. 02 (2021): 40–50. http://dx.doi.org/10.51179/tika.v6i02.486.

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The soaring cases of covid-19 prompted some countries to find solutions to save their people. One of the steps that is currently being taken is with vaccines. Several leading companies in the world that produce drugs are known to have produced vaccines for covid-19, one of which is AstraZeneca. AstraZeneca vaccine is known as the most widely used vaccine in all countries in the world. Interesting thing to research is how the development of the company's stock engaged in the medical field, especially companies that produce vaccines for covid-19. This study used Fourier's approach to modeling it

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WANG, YU-HUI, QING-XIAN WU, CHANG-SHENG JIANG, YA-LI XUE, and WEI FANG. "MODIFIED LEVENBERG-MARQUARDT METHOD FOR RÖSSLER CHAOTIC SYSTEM FUZZY MODELING TRAINING." International Journal of Modern Physics B 23, no. 24 (2009): 4953–61. http://dx.doi.org/10.1142/s0217979209053278.

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Generally, fuzzy approximation models require some human knowledge and experience. Operator's experience is involved in the mathematics of fuzzy theory as a collection of heuristic rules. The main goal of this paper is to present a new method for identifying unknown nonlinear dynamics such as Rössler system without any human knowledge. Instead of heuristic rules, the presented method uses the input-output data pairs to identify the Rössler chaotic system. The training algorithm is a modified Levenberg-Marquardt (L-M) method, which can adjust the parameters of each linear polynomial and fuzzy m

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Solikhun and Nanda Amalya. "Algoritma Backpropagation Metode Levenberg Marquardt Dalam Memprediksi Penyakit Stroke." Bulletin of Computer Science Research 3, no. 2 (2023): 191–96. http://dx.doi.org/10.47065/bulletincsr.v3i2.229.

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In Southeast Asia, stroke is the third leading cause of disability and the disease with the second highest risk of death. A stroke occurs when a blood vessel in the brain is blocked or bursts, preventing some cells or brain tissue from receiving the oxygen they need from the blood supply. This study focuses on predicting stroke using the Levenberg Marquardt algorithm. Stroke prediction data is taken from the Kaggle website which consists of 5110 records. The attributes used to predict stroke consist of 10 attributes, namely gender, age, patient hypertension, heart disease, marital status, type

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Hao, Zhao Ming, Jing Yi Du, and Zhi Chen Zheng. "A Research on Methane Prediction Model Based on Improved BP-GA Network." Applied Mechanics and Materials 239-240 (December 2012): 1382–86. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.1382.

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The amount of methane emission is crucial to the safety of coal mine. The paper proposes the Levenberg Marquardt (LM) algorithm (in the nonlinear least squares algorithms) that can reduce the training time of BP network. Genetic Algorithm (GA) is used to optimize weights in global search to prevent the inherent defects that neural network is liable to get stuck in local minimal points. Furthermore, neural network can prevent the defect of weak GA local search. Finally, BP-GA modal was trained and the sample data were precisely analyzed, which proves that this model features broad adaptability

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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 segme

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Turčaník, Michal, and J. Baráth. "Detection of Malicious Network Activity by Artificial Neural Network." Advances in Military Technology 18, no. 1 (2023): 103–20. http://dx.doi.org/10.3849/aimt.01794.

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This paper presents a deep learning approach to detect malicious communication in a computer network. The intercepted communication is transformed into behavioral feature vectors that are reduced (using principal component analysis and stepwise selection methods) and normalized to create training and test sets. A feed-forward artificial neural network is then used as a classifier to determine the type of malicious communication. Three training algorithms were used to train the neural network: the Levenberg-Marquardt algorithm, Bayesian regularization, and the scaled conjugate gradient backprop

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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 A

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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. Leve

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Ibn Ibrahimy, Muhammad, Rezwanul Ahsan, and Othman Omran Khalifa. "Design and Optimization of Levenberg-Marquardt based Neural Network Classifier for EMG Signals to Identify Hand Motions." Measurement Science Review 13, no. 3 (2013): 142–51. http://dx.doi.org/10.2478/msr-2013-0023.

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This paper presents an application of artificial neural network for the classification of single channel EMG signal in the context of hand motion detection. Seven statistical input features that are extracted from the preprocessed single channel EMG signals recorded for four predefined hand motions have been used for neural network classifier. Different structures of neural network, based on the number of hidden neurons and two prominent training algorithms, have been considered in the research to find out their applicability for EMG signal classification. The classification performances are a

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Bar, Nirjhar, and Sudip Kumar Das. "Modeling of Gas Holdup and Pressure Drop Using ANN for Gas-Non-Newtonian Liquid Flow in Vertical Pipe." Advanced Materials Research 917 (June 2014): 244–56. http://dx.doi.org/10.4028/www.scientific.net/amr.917.244.

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This paper is an attempt to compare the the performance of the three different Multilayer Perceptron training algorithms namely Backpropagation, Scaled Conjugate Gradient and Levenberg-Marquardt for the prediction of the gas hold up and frictional pressure drop across the vertical pipe for gas non-Newtonian liquid flow from our earlier experimental data. The Multilayer Perceptron consists of a single hidden layer. Four different transfer functions were used in the hidden layer. All three algorithms were useful to predict the gas holdup and frictional pressure drop across the vertical pipe. Sta

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Shah, Habib, Rozaida Ghazali, Nazri Mohd Nawi, and Mustafa Mat Deris. "G-HABC Algorithm for Training Artificial Neural Networks." International Journal of Applied Metaheuristic Computing 3, no. 3 (2012): 1–19. http://dx.doi.org/10.4018/jamc.2012070101.

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Learning problems for Neural Network (NN) has widely been explored in the past two decades. Researchers have focused more on population-based algorithms because of its natural behavior processing. The population-based algorithms are Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and recently Hybrid Ant Bee Colony (HABC) algorithm produced an easy way for NN training. These social based techniques are mostly used for finding best weight values and over trapping local minima in NN learning. Typically, NN trained by traditional approach, namely the Backpropagation (BP) algorithm, has

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Liu, Shao Man, and Zhao Lin Wu. "Identification of Three Dimension Vibration System Based on L-M Algorithm with Learning Rate." Advanced Materials Research 479-481 (February 2012): 1225–29. http://dx.doi.org/10.4028/www.scientific.net/amr.479-481.1225.

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The vibration control platform is designed involving periodical micro- vibration in Three Dimension (3D) scanning system. The system is identified utilizing neural network based on Levenberg - Marquardt (L-M) algorithm. The L-M algorithm is the combination of the steepest decent algorithm with the Gauss - Newton algorithm so that it has the faster speed of convergence and higher approach accuracy. Compared with the rational BP, the simulation result showed the LM algorithm with learning rate speed up learning process, reduce training time with improve identification accuracy greatly. The ident

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Lin, Jyh-Woei, Chun-Tang Chao, and Juing-Shian Chiou. "Backpropagation neural network as earthquake early warning tool using a new modified elementary Levenberg–Marquardt Algorithm to minimise backpropagation errors." Geoscientific Instrumentation, Methods and Data Systems 7, no. 3 (2018): 235–43. http://dx.doi.org/10.5194/gi-7-235-2018.

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Abstract. A new modified elementary Levenberg–Marquardt Algorithm (M-LMA) was used to minimise backpropagation errors in training a backpropagation neural network (BPNN) to predict the records related to the Chi-Chi earthquake from four seismic stations: Station-TAP003, Station-TAP005, Station-TCU084, and Station-TCU078 belonging to the Free Field Strong Earthquake Observation Network, with the learning rates of 0.3, 0.05, 0.2, and 0.28, respectively. For these four recording stations, the M-LMA has been shown to produce smaller predicted errors compared to the Levenberg–Marquardt Algorithm (L

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Si, Tapas, and Ramkinkar Dutta. "Partial Opposition-Based Particle Swarm Optimizer in Artificial Neural Network Training for Medical Data Classification." International Journal of Information Technology & Decision Making 18, no. 05 (2019): 1717–50. http://dx.doi.org/10.1142/s0219622019500329.

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This paper presents an improved opposition-based Particle Swarm Optimizer (PSO) with partial opposition-based learning. The partial opposition-based learning scheme is a new form of opposition-based learning and it is employed to improve the performance. Nowadays, the artificial neural network (ANN), an important machine learning tool, is used in medicine especially in medical disease diagnosis. ANN training is a complex task and a training algorithm has a significant role in ANN’s performance. Therefore, the proposed algorithm is applied in training of Multi-Layer Feed-Forward Neural Network

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ABDULLAH, LAZIM, and HERRINI MOHD PAUZI. "AN EFFECTIVE MODEL FOR CARBON DIOXIDE EMISSIONS PREDICTION: COMPARISON OF ARTIFICIAL NEURAL NETWORKS LEARNING ALGORITHMS." International Journal of Computational Intelligence and Applications 13, no. 03 (2014): 1450014. http://dx.doi.org/10.1142/s146902681450014x.

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This paper intends to compare various learning algorithms available for training the multi-layer perceptron (MLP) type of artificial neural networks (ANNs). By using different learning algorithms, this study investigates the performances of gradient descent (GD) algorithm; Levenberg-Marquardt (LM) algorithm; and also Boyden, Fletcher, Goldfarb and Shannon (BFGS) algorithm to predict the emissions of carbon dioxide ( CO 2) in Malaysia. The impact factors of emissions, such as energy use; gross domestic product per capita; population density; combustible renewable and waste; also CO 2 intensity

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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, Sca

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OKKAN, Umut. "Application of Levenberg-Marquardt Optimization Algorithm Based Multilayer Neural Networks for Hydrological Time Series Modeling." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 1, no. 1 (2011): 53–63. http://dx.doi.org/10.11121/ijocta.01.2011.0038.

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It is very important to make hydrological time series modeling on water resource engineering and the decision making strategies of water resource management. In this paper, a comprehensive study on the application of Levenberg-Marquardt optimization algorithms based Multilayer Neural Networks in the monthly inflows of Demirkopru Dam, which is located in the Gediz Basin/Turkey, is presented. The best network structure which requires monthly areal precipitation, temperature and one month ahead areal precipitation values as the input data, is trained by using the 30 years monthly time series. The

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Batur, Maryna, and Kateryna Babii. "The performance analysis of deep learning algorithms for modelling and forecasting the particulate matter (PM₁₀) in the eastern part of Turkey." IOP Conference Series: Earth and Environmental Science 1348, no. 1 (2024): 012046. http://dx.doi.org/10.1088/1755-1315/1348/1/012046.

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Abstract The main purpose of this study is to select the most reliable nonlinear computational model to predict the particulate matter (PM10) concentrations. Time series data of three years PM10 concentrations were used as input variable. For the prediction, three different types of dynamic nonlinear autoregressive models were built and compared. These models are the Levenberg-Marquardt algorithm, the Bayesian Regulization algorithm, and the Scaled Conjugate Gradient algorithm. For each of these algorythms, various settings were adopted with the subsequent statistical analysis. To analyse the

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Hussein, Areeg F., and Hanan A. R. Akkar. "Intelligent controller Design based on wind-solar system." Engineering and Technology Journal 39, no. 2A (2021): 326–37. http://dx.doi.org/10.30684/etj.v39i2a.1761.

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This paper presents an Intelligent controller designed to mastery the output power flow from the Solar System, the Wind system, the sum of the two systems or from the battery system, according to the Maximum power point tracking algorithm, to ensure the continuity of the output power at fast time response. The proposed controller has been designed using MATLAB m-file and trained with the different number of hidden neurons using two different algorithms to get as fast a response time with minimum Mean Square Error (MSE) as possible which resulted in six hidden neurons using Levenberg-Marquardt

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Mota, Tiago, Jorgean Leal, and Antônio Lima. "Fast Fading Channel Neural Equalization Using Levenberg-Marquardt Training Algorithm and Pulse Shaping Filters." International Journal of Communications, Network and System Sciences 07, no. 02 (2014): 71–74. http://dx.doi.org/10.4236/ijcns.2014.72008.

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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

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Fairbank, Michael, and Eduardo Alonso. "Efficient Calculation of the Gauss-Newton Approximation of the Hessian Matrix in Neural Networks." Neural Computation 24, no. 3 (2012): 607–10. http://dx.doi.org/10.1162/neco_a_00248.

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The Levenberg-Marquardt (LM) learning algorithm is a popular algorithm for training neural networks; however, for large neural networks, it becomes prohibitively expensive in terms of running time and memory requirements. The most time-critical step of the algorithm is the calculation of the Gauss-Newton matrix, which is formed by multiplying two large Jacobian matrices together. We propose a method that uses backpropagation to reduce the time of this matrix-matrix multiplication. This reduces the overall asymptotic running time of the LM algorithm by a factor of the order of the number of out

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Prasad, Bhawesh, Raj Kumar, and Manmohan Singh. "Performance analysis of various training algorithms of deep learning based controller." Engineering Research Express 5, no. 2 (2023): 025038. http://dx.doi.org/10.1088/2631-8695/acd3d5.

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Abstract Advances in artificial neural networks (ANN), specifically deep learning (DL), have widened the application domain of process control. DL algorithms and models have become quite common these days. The training algorithm is the most important part of an ANN that affects the performance of the controller. Training algorithms optimize the weights and biases of the ANN according to the input-output patterns. In this paper, the performance of different training algorithms was evaluated, analysed, and compared in a feed-forward backpropagation architecture. The training algorithms were simu

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Solikhun, Solikhun, and Sundari Putri Lestari. "Pengujian Jaringan Saraf Tiruan Dalam Mendiagnosa Gangguan Jiwa Menggunakan Algoritma Backpropogation Levenberg-Marquardt." Journal of Information System Research (JOSH) 4, no. 3 (2023): 920–27. http://dx.doi.org/10.47065/josh.v4i3.3285.

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Mental disorders are mental health issues that make it hard to meet one's own or other people's needs. A person's life may be affected by changes in behavior brought on by this condition. To conquer this issue, a backpropagation calculation has been created to help with distinguishing mental problems. This calculation utilizes information got from mental tests to distinguish early indications of mental problems in an individual. With this calculation, psychological wellness experts can settle on additional quick and precise symptomatic choices. The Levenberg-Marquadt method and the backpropoga

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Kacimi, Houda, Sara Fennane, Hamza Mabchour, Fatehi ALtalqi, and Adil Echchelh. "Evaluation of Nonlinear Autoregressive Network with Exogenous Inputs Architectures for Wind Speed forecasting." EPJ Web of Conferences 326 (2025): 05003. https://doi.org/10.1051/epjconf/202532605003.

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This research investigates the optimal NARX neural network architecture for forecasting daily maximum wind speed in Dakhla, a region with substantial wind energy resources. Two configurations NARX-SP (open loop) and NARX-P (closed loop) were evaluated using the Levenberg-Marquardt algorithm, known for its fast and efficient training. Predictive performance was assessed using RMSE to measure the gap between predicted and actual values. Results show that NARX-SP outperforms NARX-P, achieving lower RMSE and better forecasting accuracy.

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Rao, Sivakavi Naga Venkata Bramareswara, Venkata Pavan Kumar Yellapragada, Kottala Padma, et al. "Day-Ahead Load Demand Forecasting in Urban Community Cluster Microgrids Using Machine Learning Methods." Energies 15, no. 17 (2022): 6124. http://dx.doi.org/10.3390/en15176124.

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The modern-day urban energy sector possesses the integrated operation of various microgrids located in a vicinity, named cluster microgrids, which helps to reduce the utility grid burden. However, these cluster microgrids require a precise electric load projection to manage the operations, as the integrated operation of multiple microgrids leads to dynamic load demand. Thus, load forecasting is a complicated operation that requires more than statistical methods. There are different machine learning methods available in the literature that are applied to single microgrid cases. In this line, th

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Ngia, L. S. H., and J. Sjoberg. "Efficient training of neural nets for nonlinear adaptive filtering using a recursive Levenberg-Marquardt algorithm." IEEE Transactions on Signal Processing 48, no. 7 (2000): 1915–27. http://dx.doi.org/10.1109/78.847778.

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Bascil, M. Serdar, and Feyzullah Temurtas. "A Study on Hepatitis Disease Diagnosis Using Multilayer Neural Network with Levenberg Marquardt Training Algorithm." Journal of Medical Systems 35, no. 3 (2009): 433–36. http://dx.doi.org/10.1007/s10916-009-9378-2.

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Balli, Serkan, and Faruk Sen. "Performance evaluation of artificial neural networks for identification of failure modes in composite plates." Materials Testing 63, no. 6 (2021): 565–70. http://dx.doi.org/10.1515/mt-2020-0094.

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Abstract The aim of this work is to identify failure modes of double pinned sandwich composite plates by using artificial neural networks learning algorithms and then analyze their accuracies for identification. Mechanically pinned specimens with two serial pins/bolts for sandwich composite plates were used for recognition of failure modes which were obtained in previous experimental studies. In addition, the empirical data of the preceding work was determined with various geometric parameters for various applied preload moments. In this study, these geometric parameters and fastened/bolted jo

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Gospodinova, Ekaterina, and Dimitar Nenov. "Mathematical Modeling based on Neural Network Learning for Object Recognition in Automated Systems." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 19 (December 31, 2024): 427–35. https://doi.org/10.37394/23203.2024.19.46.

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This paper aims to identify efficient methods of mathematically modeling an automated physical system using a neural network. Based on the Levenberg-Marquardt method, we built a feed-forward neural network with the capabilities of a graphics accelerator. The model also sums up and suggests a new neural network training algorithm with Bayes regularization, Nguyen-Widrow initialization, and the early stopping and control method. This greatly expands the efficiency of solving problems where knowledge of an automation system is usable.

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Eker, Erdal, Murat Kayri, Serdar Ekinci, and Davut İzci. "Comparison of Swarm-based Metaheuristic and Gradient Descent-based Algorithms in Artificial Neural Network Training." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 12, no. 1 (2023): e29969. http://dx.doi.org/10.14201/adcaij.29969.

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This paper aims to compare the gradient descent-based algorithms under classical training model and swarm-based metaheuristic algorithms in feed forward backpropagation artificial neural network training. Batch weight and bias rule, Bayesian regularization, cyclical weight and bias rule and Levenberg-Marquardt algorithms are used as the classical gradient descent-based algorithms. In terms of the swarm-based metaheuristic algorithms, hunger games search, gray wolf optimizer, Archimedes optimization, and the Aquila optimizer are adopted. The Iris data set is used in this paper for the training.

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Trzepieciński, Tomasz, and Marcin Szpunar. "Multivariate Modelling of Effectiveness of Lubrication of Ti-6al-4v Titanium Alloy Sheet using Vegetable Oil-Based Lubricants." Advances in Materials Science 21, no. 2 (2021): 26–39. http://dx.doi.org/10.2478/adms-2021-0009.

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Abstract The article presents the results of modelling the friction phenomenon using artificial neural networks and analysis of variance. The test material was composed of strip specimens made of 0.5-mm-thick alpha-beta Grade 5 (Ti-6Al-4V) titanium alloy sheet. A special tribotester was used in the tests to simulate the friction conditions between the punch and the sheet metal in the sheet metal forming process. A test called the strip drawing test has been conducted in conditions in which the sheet surface is lubricated with six environmentally friendly oils (palm, coconut, olive, sunflower,

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