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Journal articles on the topic 'Algoritmus Levenberg-Marquardt'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Zhang, J. Z., and L. H. Chen. "Nonmonotone Levenberg–Marquardt Algorithms and Their Convergence Analysis." Journal of Optimization Theory and Applications 92, no. 2 (1997): 393–418. http://dx.doi.org/10.1023/a:1022615415582.

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2

Basterrech, S., S. Mohammed, G. Rubino, and M. Soliman. "Levenberg--Marquardt Training Algorithms for Random Neural Networks." Computer Journal 54, no. 1 (2009): 125–35. http://dx.doi.org/10.1093/comjnl/bxp101.

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3

Merdekawati, Gema Indah, and Ismail. "PREDIKSI CURAH HUJAN DI JAKARTA BERBASIS ALGORITMA LEVENBERG MARQUARDT." Jurnal Ilmiah Informatika Komputer 24, no. 2 (2019): 116–28. http://dx.doi.org/10.35760/ik.2019.v24i2.2366.

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Perubahan pola curah hujan yang tidak menentu sangat berpengaruh terhadap berbagai aspek kehidupan terutama di kota Jakarta dimana segala aktivitas penting berada di dalamnya, sehingga perlu dilakukan prediksi curah hujan agar tidak mengganggu aktifitas penting dan harus segera dilaksanakan. Penelitian ini akan melakukan prediksi curah hujan di Jakarta berbasis algoritma levenberg marquardt menggunakan data curah hujan harian mulai dari 1 Mei 2016 – 30 April 2018 dari stasiun meteorology Kemayoran. Penelitian ini terdiri dari beberapa tahap, yakni pengolahan data curah hujan harian, normalisas

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Izmailov, Alexey, Alexey Kurennoy, and Petr Stetsyuk. "Levenberg–Marquardt method for unconstrained optimization." Tambov University Reports. Series: Natural and Technical Sciences, no. 125 (2019): 60–74. http://dx.doi.org/10.20310/1810-0198-2019-24-125-60-74.

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We propose and study the Levenberg–Marquardt method globalized by means of linesearch for unconstrained optimization problems with possibly nonisolated solutions. It is well-recognized that this method is an efficient tool for solving systems of nonlinear equations, especially in the presence of singular and even nonisolated solutions. Customary globalization strategies for the Levenberg–Marquardt method rely on linesearch for the squared Euclidean residual of the equation being solved. In case of unconstrained optimization problem, this equation is formed by putting the gradient of the object

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SIRAJUDDIN, HAJI. "KOMPARASI LEVENBERG-MARQUARDT (LM) DENGAN BROYDEN, FLETCHER, GOLDFARB, AND SHANNO QUASI-NEWTON (BFGS) BPNN UNTUK DIIMPLEMENTASIKAN PADA DATA KECEPATAN ANGIN." Technologia: Jurnal Ilmiah 8, no. 2 (2017): 90. http://dx.doi.org/10.31602/tji.v8i2.1112.

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Penelitian ini dilakukan untuk mengkomparasi Levenberg-Marquardt (Lm) Dengan Broyden, Fletcher, Goldfarb, And Shanno Quasi-Newton (Bfgs) pada Penerapan Backpropagation Neural Network (BPNN) untuk memprediksi data Kecepatan Angin rata-rata. Data yang digunakan pada penelitian ini adalah data kecepatan angin rata-rata harian pada bulan Januari 2010 sampai Desember 2014 di Banjarbaru, Kalimantan selatan . Kecepatan angin ditentukan oleh perbedaan tekanan udara antara tempat asal dan tujuan angin dan daerah yang dilaluinya, Prediksi salah satu teknik yang paling penting dalam mengetahui kecepatan

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Lyn Dee, Goh, Norhisham Bakhary, Azlan Abdul Rahman, and Baderul Hisham Ahmad. "A Comparison of Artificial Neural Network Learning Algorithms for Vibration-Based Damage Detection." Advanced Materials Research 163-167 (December 2010): 2756–60. http://dx.doi.org/10.4028/www.scientific.net/amr.163-167.2756.

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This paper investigates the performance of Artificial Neural Network (ANN) learning algorithms for vibration-based damage detection. The capabilities of six different learning algorithms in detecting damage are studied and their performances are compared. The algorithms are Levenberg-Marquardt (LM), Resilient Backpropagation (RP), Scaled Conjugate Gradient (SCG), Conjugate Gradient with Powell-Beale Restarts (CGB), Polak-Ribiere Conjugate Gradient (CGP) and Fletcher-Reeves Conjugate Gradient (CGF) algorithms. The performances of these algorithms are assessed based on their generalisation capab

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Çavuşlu, Mehmet Ali, and Suhap Şahin. "FPGA IMPLEMENTATION OF ANN TRAINING USING LEVENBERG AND MARQUARDT ALGORITHMS." Neural Network World 28, no. 2 (2018): 161–78. http://dx.doi.org/10.14311/nnw.2018.28.010.

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Du, Shou-qiang, and Yan Gao. "Convergence analysis of nonmonotone Levenberg–Marquardt algorithms for complementarity problem." Applied Mathematics and Computation 216, no. 5 (2010): 1652–59. http://dx.doi.org/10.1016/j.amc.2010.03.021.

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9

Koh, Jin Ming, and Kang Hao Cheong. "Automated electron-optical system optimization through switching Levenberg–Marquardt algorithms." Journal of Electron Spectroscopy and Related Phenomena 227 (August 2018): 31–39. http://dx.doi.org/10.1016/j.elspec.2018.05.009.

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10

Mendis, B. S. U., and T. D. Gedeon. "WRAO and OWA learning using Levenberg–Marquardt and genetic algorithms." Memetic Computing 3, no. 2 (2010): 101–10. http://dx.doi.org/10.1007/s12293-010-0054-3.

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11

Reyes-Ramos, Fidel, and J. O. Campos-Enriquez. "Enhancing C2 and C3 coherency resolutions through optimizing semblance-based functions." Geofísica Internacional 46, no. 3 (2007): 163–74. http://dx.doi.org/10.22201/igeof.00167169p.2007.46.3.37.

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Se calculan echados aparentes de reflectores 3-D maximizando la coherencia basada en la semblanza (C2) mediante técnicas de optimización numérica. Esta maximización fue hecha por medio de una búsqueda en una partición del dominio de los echados aparentes, y por medio de algoritmos de optimización. Se aplicaron los algoritmos simplex y Levenberg-Marquardt, cuyos desempeños fueron comparados con aquellos de las técnicas directas. De acuerdo a experimentos numéricos con datos reales, el algoritmo simplex permite no solamente importante ahorros en tiempos de cómputo, sino que proporciona también l

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Bilski, Jarosław, Bartosz Kowalczyk, Alina Marchlewska, and Jacek M. Zurada. "Local Levenberg-Marquardt Algorithm for Learning Feedforwad Neural Networks." Journal of Artificial Intelligence and Soft Computing Research 10, no. 4 (2020): 299–316. http://dx.doi.org/10.2478/jaiscr-2020-0020.

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AbstractThis paper presents a local modification of the Levenberg-Marquardt algorithm (LM). First, the mathematical basics of the classic LM method are shown. The classic LM algorithm is very efficient for learning small neural networks. For bigger neural networks, whose computational complexity grows significantly, it makes this method practically inefficient. In order to overcome this limitation, local modification of the LM is introduced in this paper. The main goal of this paper is to develop a more complexity efficient modification of the LM method by using a local computation. The introd

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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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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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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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16

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 eff

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Hartono, Arif Marifa Ahmad, and M. Sadikin. "Comparison methods of short term electrical load forecasting." MATEC Web of Conferences 218 (2018): 01002. http://dx.doi.org/10.1051/matecconf/201821801002.

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The supply of electricity that exceeds the load requirement results in the occurrence of electrical power losses. To provide the appropriate power supply to these needs, there must be a plan for the provision of electricity by making prediction or estimation of electrical load. Therefore the issue of electrical load forecasting becomes very important in the provision of efficient power. In this study, the author tries to build a model of short-term electrical load prediction using artificial neural network (ANN) with learning algorithm levenberg-marquardt (Trainlm), Bayesian regularization (Tr

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18

Boateng, Cyril D., Li-Yun Fu, Wu Yu, and Guan Xizhu. "Porosity inversion by Caianiello neural networks with Levenberg-Marquardt optimization." Interpretation 5, no. 3 (2017): SL33—SL42. http://dx.doi.org/10.1190/int-2016-0119.1.

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Caianiello neural networks (CNNs) incorporated with the Robinson seismic convolutional model are modified by the Levenberg-Marquardt algorithm to improve convergence. CNNs are extended to the multiattribute domain for reservoir property inversion, with time-varying signal processing by a frequency-domain block implementation using fast Fourier transforms. Optimal inversion can be achieved by applying the Levenberg-Marquardt optimization to multiattribute domain CNNs for convergency improvement due to its ability to swing between the steepest-descent and Gauss-Newton algorithms. The methodology

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19

SAKURAI, AKITO. "A FAST AND CONVERGENT STOCHASTIC MLP LEARNING ALGORITHM." International Journal of Neural Systems 11, no. 06 (2001): 573–83. http://dx.doi.org/10.1142/s0129065701000977.

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We propose a stochastic learning algorithm for multilayer perceptrons of linear-threshold function units, which theoretically converges with probability one and experimentally exhibits 100% convergence rate and remarkable speed on parity and classification problems with typical generalization accuracy. For learning the n bit parity function with n hidden units, the algorithm converged on all the trials we tested (n=2 to 12) after 5.8· 4.1n presentations for 0.23· 4.0n-6 seconds on a 533MHz Alpha 21164A chip on average, which is five to ten times faster than Levenberg-Marquardt algorithm with r

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Tananaev, N. I. "Fitting sediment rating curves using regression analysis: a case study of Russian Arctic rivers." Proceedings of the International Association of Hydrological Sciences 367 (March 3, 2015): 193–98. http://dx.doi.org/10.5194/piahs-367-193-2015.

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Abstract. Published suspended sediment data for Arctic rivers is scarce. Suspended sediment rating curves for three medium to large rivers of the Russian Arctic were obtained using various curve-fitting techniques. Due to the biased sampling strategy, the raw datasets do not exhibit log-normal distribution, which restricts the applicability of a log-transformed linear fit. Non-linear (power) model coefficients were estimated using the Levenberg-Marquardt, Nelder-Mead and Hooke-Jeeves algorithms, all of which generally showed close agreement. A non-linear power model employing the Levenberg-Mar

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21

Widodo, Widodo, and Durra Handri Saputera. "Improving Levenberg-Marquardt Algorithm Inversion Result Using Singular Value Decomposition." Earth Science Research 5, no. 2 (2016): 20. http://dx.doi.org/10.5539/esr.v5n2p20.

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Inversion is a process to determine model parameters from data. In geophysics this process is very important because subsurface image is obtained from this process. There are many inversion algorithms that have been introduced and applied in geophysics problems; one of them is Levenberg-Marquardt (LM) algorithm. In this paper we will present one of LM algorithm application in one-dimensional magnetotelluric (MT) case. The LM algorithm used in this study is improved version of LM algorithm using singular value decomposition (SVD). The result from this algorithm is then compared with the algorit

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França, José Alexandre de, Maria Bernadete de Morais França, Marcela Hitomi Koyama, and Tiago Polizer da Silva. "Uma implementação do algoritmo Levenberg-Marquardt dividido para aplicações em visão computacional." Semina: Ciências Exatas e Tecnológicas 30, no. 1 (2009): 51. http://dx.doi.org/10.5433/1679-0375.2009v30n1p51.

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Mustafidah, Hindayati, Muhamad Zaeni Budiastanto, and Suwarsito Suwarsito. "Kinerja Algoritma Pelatihan Levenberg-Marquardt dalam Variasi Banyaknya Neuron pada Lapisan Tersembunyi." JUITA : Jurnal Informatika 7, no. 2 (2019): 115. http://dx.doi.org/10.30595/juita.v7i2.5863.

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Brito Júnior, C. A. R., E. M. Bezerra, L. C. Pardini, et al. "Redes neurais artificiais aplicadas para a predição do comportamento dinâmico-mecânico de compósitos de matriz epóxi reforçados com fibras de carbono." Matéria (Rio de Janeiro) 12, no. 2 (2007): 346–57. http://dx.doi.org/10.1590/s1517-70762007000200013.

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Neste trabalho, o algoritmo Levenberg-Marquardt foi aplicado para predizer o comportamento dinâmico-mecânico de compósitos de matriz epóxi reforçados com fibras de carbono. Empregou-se o ensaio de vibração amortecida (ASTM E-756) para viga do tipo engastada-livre que forneceu experimentalmente as curvas de amplitude em função do tempo de resposta. O compósito é de uso aeronáutico tendo configuração [0º/45º/90º/0º]S. Uma rede neural do tipo "perceptron" de múltiplas camadas foi empregada, e os resultados mostraram que a aplicação do algoritmo de aprendizado Levenberg-Marquardt conduz a uma elev

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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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Aguiar, Maria, Tiago Alves, Leonardo Honório, Ivo Junior, and Vinícius F. Vidal. "Performance Evaluation of Bundle Adjustment with Population Based Optimization Algorithms Applied to Panoramic Image Stitching." Sensors 21, no. 15 (2021): 5054. http://dx.doi.org/10.3390/s21155054.

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The image stitching process is based on the alignment and composition of multiple images that represent parts of a 3D scene. The automatic construction of panoramas from multiple digital images is a technique of great importance, finding applications in different areas such as remote sensing and inspection and maintenance in many work environments. In traditional automatic image stitching, image alignment is generally performed by the Levenberg–Marquardt numerical-based method. Although these traditional approaches only present minor flaws in the final reconstruction, the final result is not a

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Meramo-Hurtado, Samir, Plinio Puello, and Julio Rodriguez. "Application of Solution Strategies for Numerical Estimation of Thermodynamic Equilibrium Parameters for an Acetone–Butanol Mixture." Applied Sciences 10, no. 9 (2020): 3136. http://dx.doi.org/10.3390/app10093136.

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The development of reliable numerical estimation of thermodynamic parameters is a crucial aspect in the ongoing research about process engineering and design. The consideration of these concepts lets to design more precise processing units and separations stages based on the predicted nature of substances. Therefore, this study presents an application of different solution methods for the estimation of thermodynamic equilibrium parameters of an acetone–butanol mixture. This dissolution is a non-ideal system, so, the non-ideal Raoult’s Law and Wilson’s equation were used to model the liquid–vap

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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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Kokot, Seweryn, and Zbigniew Zembaty. "Damage reconstruction of 3D frames using genetic algorithms with Levenberg–Marquardt local search." Soil Dynamics and Earthquake Engineering 29, no. 2 (2009): 311–23. http://dx.doi.org/10.1016/j.soildyn.2008.03.001.

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TRIBUZY-NETO, IVAN A., KATRINE G. CONCEIÇÃO, FLAVIA K. SIQUEIRA-SOUZA,, and CARLOS EC FREITAS. "Relación longitud-peso en once especies de peces de los lagos de planos inundables amazónicos." Revista Colombiana de Ciencia Animal - RECIA 7, no. 1 (2015): 77. http://dx.doi.org/10.24188/recia.v7.n1.2015.425.

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Este estudio determinó la relación longitud-peso de once especies de peces de la Amazonia, capturados en lagos de várzea, en los años 2004, 2005 y 2006. Los parámetros de relación longitud-peso fueron obtenidos por estimación no lineal, usando el algoritmo de Levenberg-Marquardt. Fue observado que apenas una especie (Mylossoma duriventre) exhibió crecimiento isométrico.

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31

Smolik, Waldemar, and Jacek Kryszyn. "LINEAR OVER RANGES ITERATIVE ALGORITHMS FOR IMAGE RECONSTRUCTION IN ELECTRICAL CAPACITANCE TOMOGRAPHY." Informatics Control Measurement in Economy and Environment Protection 7, no. 1 (2017): 115–20. http://dx.doi.org/10.5604/01.3001.0010.4598.

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The paper concerns the non-linear algorithms for image reconstruction in electrical capacitance tomography for which Jacobi matrix computation time is very long. The paper presents the idea of an iterative linearization in nonlinear problems, which leads to a reduction in the number of steps calculating Jacobi matrix. The linear Landweber algorithm with sensitivity matrix updating and non-linear Levenberg-Marquardt algorithm with Jacobi matrix updating in selected steps only were presented.

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Adler, John, and Tulus Bangkit Pratama. "Identifikasi Pola Warna Citra Google Maps Menggunakan Jaringan Syaraf Tiruan Metode Levenberg –Marquardt dengan MatLab Versi 7.8." Komputika : Jurnal Sistem Komputer 7, no. 2 (2018): 95–101. http://dx.doi.org/10.34010/komputika.v7i2.1396.

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Riset mengenai Jaringan Syaraf Tiruan telah banyak diimplementasikan dalam bidang keilmuan, khususnya Geografi, yang mengidentifikasi pola citra peta untuk membedakan kenampakan geografis, dan diterapkan pada Google Maps. Proses pengolahan citra, dengan memperbaiki kualitas citra agar mudah diinterpretasi oleh manusia atau mesin (komputer). Teknik yang digunakan adalah teknik Segmentasi. Metode training menggunakan algoritma Levenberg-Marquardt. Hasil penelitian pada proses training diperoleh nilai epoch sebanyak 17 dari 100 iterasi. Waktu yang digunakan selama proses training sebanyak 6 detik

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Parra Márquez, Juan Carlos. "Análisis del comportamiento del Modelo de Crecimiento de Gompertz en la predicción del crecimiento de la economía de Argentina, Bolivia, Chile y Perú." Estudios de Economía Aplicada 35, no. 2 (2019): 443. http://dx.doi.org/10.25115/eea.v35i2.2488.

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El objetivo de este trabajo es predecir el crecimiento económico de Argentina, Bolivia, Chile y Perú a través del modelo de crecimiento biológico de Gompertz. Además, se propone un modelo que mejore la bondad de ajuste. Para para estimar los parámetros de ambos modelos y verificar su tasa de convergencia se utiliza los algoritmos de Gauss-Newton, Newton-Raphson y Levenberg-Marquardt. La elección del modelo de mejor aproximación se realiza según sus criterios de Akaike y Schwartz-Bayesian. Finalmente, los algoritmos se implementan en el software Matlab v. R2013b.

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Ritha, Nola, and Retantyo Wardoyo. "Implementasi Neural Fuzzy Inference System dan Algoritma Pelatihan Levenberg-Marquardt untuk Prediksi Curah Hujan." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 10, no. 2 (2016): 125. http://dx.doi.org/10.22146/ijccs.15532.

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Franco, Ricardo Augusto Pereira, Flávio Henrique Teles Vieira, Marcelo Stehling De Castro, and Getúlio Antero de Deus Júnior. "Estimação de Parâmetros de Modelo de Sistemas Fotovoltaicos Utilizando Algoritmo de Levenberg-Marquardt Modificado." Tema (São Carlos) 19, no. 1 (2018): 79. http://dx.doi.org/10.5540/tema.2018.019.01.79.

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Xiao, Zhuolei, Yerong Zhang, Kaixuan Zhang, Dongxu Zhao, and Guan Gui. "GARLM: Greedy Autocorrelation Retrieval Levenberg–Marquardt Algorithm for Improving Sparse Phase Retrieval." Applied Sciences 8, no. 10 (2018): 1797. http://dx.doi.org/10.3390/app8101797.

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The goal of phase retrieval is to recover an unknown signal from the random measurements consisting of the magnitude of its Fourier transform. Due to the loss of the phase information, phase retrieval is considered as an ill-posed problem. Conventional greedy algorithms, e.g., greedy spare phase retrieval (GESPAR), were developed to solve this problem by using prior knowledge of the unknown signal. However, due to the defect of the Gauss–Newton method in the local convergence problem, especially when the residual is large, it is very difficult to use this method in GESPAR to efficiently solve

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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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Cui, Wenhui, Wei Qu, Min Jiang, and Gang Yao. "The atmospheric model of neural networks based on the improved Levenberg-Marquardt algorithm." Open Astronomy 30, no. 1 (2021): 24–35. http://dx.doi.org/10.1515/astro-2021-0003.

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Abstract Traditional atmospheric models are based on the analysis and fitting of various factors influencing the space atmosphere density. Neural network models do not specifically analyze the polynomials of each influencing factor in the atmospheric model, but use large data sets for network construction. Two traditional atmospheric model algorithms are analyzed, the main factors affecting the atmospheric model are identified, and an atmospheric model based on neural networks containing various influencing factors is proposed. According to the simulation error, the Levenberg-Marquardt algorit

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Mokosuli, Lindsay, Winsy Weku, and Luther Latumakulita. "Prediksi Tingkat Kriminalitas Menggunakan Jaringan Syaraf Tiruan Backpropagation: Algoritma Levenberg Marquardt di Kota Manado Berbasis Sistem Informasi Geografi." d'CARTESIAN 3, no. 1 (2014): 89. http://dx.doi.org/10.35799/dc.3.1.2014.4032.

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Abstract The information needs about crime rate in Manado become a starfing point to conduct this research. It has been done to predict the crime rate in the city of Manado using Levenberg Marquardt algorithm with training to determine the value of learning rate and momentum constant based on the value of the smallest Mean Square Error. Then do the mapping with Crime Mapping and perform cluster the predicted results to see the effect of the crime rate among adjacent districts. The data used is the data theft in the city of Manado in 2007 until 2012. The selected target is the data theft in Aug

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Silva, Mayra Luiza Marques da, Daniel Henrique Breda Binoti, José Marinaldo Gleriani, and Helio Garcia Leite. "Ajuste do modelo de Schumacher e Hall e aplicação de redes neurais artificiais para estimar volume de árvores de eucalipto." Revista Árvore 33, no. 6 (2009): 1133–39. http://dx.doi.org/10.1590/s0100-67622009000600015.

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Objetivou-se, neste trabalho, avaliar o ajuste do modelo volumétrico de Schumacher e Hall por diferentes algoritmos, bem como a aplicação de redes neurais artificiais para estimação do volume de madeira de eucalipto em função do diâmetro a 1,30 m do solo (DAP), da altura total (Ht) e do clone. Foram utilizadas 21 cubagens de povoamentos de clones de eucalipto com DAP variando de 4,5 a 28,3 cm e altura total de 6,6 a 33,8 m, num total de 862 árvores. O modelo volumétrico de Schumacher e Hall foi ajustado nas formas linear e não linear, com os seguintes algoritmos: Gauss-Newton, Quasi-Newton, Le

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Abubakar, Adamu I., Abdullah Khan, Nazri Mohd Nawi, et al. "Studying the Effect of Training Levenberg Marquardt Neural Network by Using Hybrid Meta-Heuristic Algorithms." Journal of Computational and Theoretical Nanoscience 13, no. 1 (2016): 450–60. http://dx.doi.org/10.1166/jctn.2016.4826.

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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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Wang, Wei, Yunming Pu, and Wang Li. "A Parallel Levenberg-Marquardt Algorithm for Recursive Neural Network in a Robot Control System." International Journal of Cognitive Informatics and Natural Intelligence 12, no. 2 (2018): 32–47. http://dx.doi.org/10.4018/ijcini.2018040103.

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This article has the purpose of overcoming the shortcomings of the recursive neural network learning algorithm and the inherent delay problem on the manipulator master control system. This is by analyzing the shortcomings of LM learning algorithms based on DRNN network, an improved parallel LM algorithm is proposed. The parallel search of the damping coefficient β is found in order to reduce the number of iterations of the loop, and the algorithm is used to decompose the parameter operation and the matrix operation into the processor (core), thereby improve the learning convergence speed, and

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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

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Andriani, Yuli, Anjar Wanto, and Handrizal Handrizal. "Jaringan Saraf Tiruan dalam Memprediksi Produksi Kelapa Sawit di PT. KRE Menggunakan Algoritma Levenberg Marquardt." Prosiding Seminar Nasional Riset Information Science (SENARIS) 1 (September 30, 2019): 249. http://dx.doi.org/10.30645/senaris.v1i0.30.

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Predictions are used to determine how much the rate of increase or decrease in oil palm production at PT. Kerasaan Indonesia (KRE) in the future. This study uses Artificial Neural Networks (ANN) using the Levenberg Marquardt method. The research data is secondary data sourced from PT. Kerasaan Indonesia from 2002 to 2017. Data is divided into 2 parts, namely training data and testing data. There are 5 architectural models used in this study, 7-10-1, 7-20-1, 7-30-1, 7-40-1 and 7-50-1. Of the 5 architectural models used, the best architecture is 7-50-1 by producing an accuracy rate of 83%, MSE 1

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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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Litta, A. J., Sumam Mary Idicula, and U. C. Mohanty. "Artificial Neural Network Model in Prediction of Meteorological Parameters during Premonsoon Thunderstorms." International Journal of Atmospheric Sciences 2013 (December 23, 2013): 1–14. http://dx.doi.org/10.1155/2013/525383.

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Forecasting thunderstorm is one of the most difficult tasks in weather prediction, due to their rather small spatial and temporal extension and the inherent nonlinearity of their dynamics and physics. Accurate forecasting of severe thunderstorms is critical for a large range of users in the community. In this paper, experiments are conducted with artificial neural network model to predict severe thunderstorms that occurred over Kolkata during May 3, 11, and 15, 2009, using thunderstorm affected meteorological parameters. The capabilities of six learning algorithms, namely, Step, Momentum, Conj

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HU, CHAO, MAX Q. H. MENG, and MRINAL MANDAL. "EFFICIENT MAGNETIC LOCALIZATION AND ORIENTATION TECHNIQUE FOR CAPSULE ENDOSCOPY." International Journal of Information Acquisition 02, no. 01 (2005): 23–36. http://dx.doi.org/10.1142/s0219878905000398.

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To build a new wireless robotic capsule endoscope with external guidance for controllable and interactive GI tract examination, a sensing system is needed for tracking 3D location and 2D orientation of the capsule endoscope movement. An appropriate sensing method is to enclose a small permanent magnet in the capsule. The intensities of the magnetic field produced by the magnet in different spatial points can be measured by the magnetic sensors outside the patient's body. With the sensing data of magnetic sensor array, the 3D location and 2D orientation of the capsule can be calculated. Higher

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Heskes, Tom. "On “Natural” Learning and Pruning in Multilayered Perceptrons." Neural Computation 12, no. 4 (2000): 881–901. http://dx.doi.org/10.1162/089976600300015637.

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Several studies have shown that natural gradient descent for on-line learning is much more efficient than standard gradient descent. In this article, we derive natural gradients in a slightly different manner and discuss implications for batch-mode learning and pruning, linking them to existing algorithms such as Levenberg-Marquardt optimization and optimal brain surgeon. The Fisher matrix plays an important role in all these algorithms. The second half of the article discusses a layered approximation of the Fisher matrix specific to multilayered perceptrons. Using this approximation rather th

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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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