Relevant bibliographies by topics / Algoritmus Levenberg-Marquardt

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Algoritmus Levenberg-Marquardt'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Algoritmus Levenberg-Marquardt"

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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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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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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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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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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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Dissertations / Theses on the topic "Algoritmus Levenberg-Marquardt"

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Tománek, Daniel. "Zvýšení přesnosti a robustnosti bezdrátových lokalizačních technik." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221140.

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The main theme of this thesis are problems of locating of wireless units in localization systems represented by wireless sensor networks. The thesis describes principle of trilateration and Levenberg-Marquardt algorithm. The main theme of this thesis is simulation of behavior of localisation systems and possible optimization of localization.
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Zapletal, Marek. "Implementace a testování vybraných optimalizačních metod pro úlohy odhadu parametrů simulačních modelů." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2016. http://www.nusl.cz/ntk/nusl-254426.

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This thesis deals with design of appropriate optimization algorithms for purposes of newly developed tool Mechlab’s parameter estimation, which serves for parameter estimation of simulation models in Matlab/Simulink. Levenberg-Marquardt algorithm had been chosen among other gradient methods. On the other hand, genetic algorithm and simulated annealing had been chosen from category of soft computing techniques to be implemented. Chosen algorithms were tested on artifical problem of mechanical oscilator and also on real datasets from electronic throttle. Proposed simulated annealing worked in bo
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Samek, Martin. "Adaptivní optimální regulátory s principy umělé inteligence v prostředí MATLAB - B&R." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218211.

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Master’s thesis describes adaptive optimal controller design and it’s settings. Identification with principles of artificial intelligence and recursive least squares identification with exponential and directional forgetting are compared separately and as part of controller. Adaptive optimal controller is tested on physical model and compared with solidly adjusted PSD controller. Possibilities of implementation of adaptive optimal controller into programmable logic controller B&R are show and tested.
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Shukla, Pradyumn Kumar. "Levenberg-Marquardt Algorithms for Nonlinear Equations, Multi-objective Optimization, and Complementarity Problems." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-27372.

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The Levenberg-Marquardt algorithm is a classical method for solving nonlinear systems of equations that can come from various applications in engineering and economics. Recently, Levenberg-Marquardt methods turned out to be a valuable principle for obtaining fast convergence to a solution of the nonlinear system if the classical nonsingularity assumption is replaced by a weaker error bound condition. In this way also problems with nonisolated solutions can be treated successfully. Such problems increasingly arise in engineering applications and in mathematical programming. In this thesis we us
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Kostka, Filip. "Umělá neuronová síť pro modelování polí uvnitř automobilu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220578.

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The project deals with artificial neural networks. After designing and debugging the test data set and the training sample set, we created a multilayer perceptron network in the Neural NetworkToolbox (NNT) of Matlab. When creating networks, we used different training algorithms and algorithms improving the generalization of the network. When creating a radial basis network, we did not use the NNT, but a specific source code in Matlab was written. Functionality of neural networks was tested on simple training and testing patterns. Realistic training data were obtained by the simulation of twelv
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Shukla, Pradyumn K. [Verfasser]. "Levenberg-Marquardt Algorithms for Nonlinear Equations, Multi-objective Optimization, and Complementarity Problems / Pradyumn K Shukla." Aachen : Shaker, 2010. http://d-nb.info/1122546505/34.

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Ferreira, Ana Paula Carvalho da Silva. "Identificação do funcional da resposta aeroelástica via redes neurais artificiais." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/18/18135/tde-04022016-095107/.

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Identificação e predição do comportamento aeroelástico representa um grande desafio para a análise e controle de fenômenos aeroelásticos adversos. A modelagem aeroelástica requer informações tanto sobre a dinâmica estrutural quanto sobre o comportamento aerodinâmico não estacionário. No entanto, a maioria das metodologias disponíveis atualmente são baseadas no desacoplamento entre o modelo estrutural e o modelo aerodinâmico não estacionário. Conseqüentemente, métodos alternativos são bem vindos na área de pesquisa aerolástica. Entre os métodos alternativos está o funcional multicamada, que for
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Allen, Andy. "Analytic element modeling of the High Plains Aquifer: non-linear model optimization using Levenberg-Marquardt and particle swarm algorithms." Thesis, Kansas State University, 2012. http://hdl.handle.net/2097/14103.

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Master of Science<br>Department of Civil Engineering<br>David R. Steward<br>Accurate modeling of the High Plains Aquifer depends on the availability of good data that represents and quantities properties and processes occurring within the aquifer. Thanks to many previous studies there is a wealth of good data available for the High Plains Aquifer but one key component, groundwater-surface water interaction locations and rates, is generally missing. Without these values accurate modeling of the High Plains Aquifer is very difficult to achieve. This thesis presents methods for simplifying the m
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Lecouvé, Marc. "Conception et réalisations de filtres microondes à modes évanescents à l'aide d'un algorithme génétique : Egalisation d'amplitude par l'algorithme de levenberg-Marquardt." Bordeaux 1, 2000. http://www.theses.fr/2000BOR12211.

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Les filtres constituent un élément essentiel des systèmes de télécommunications par satellite. Filtres à guides et cavités sont le plus souvent utilisés en raison des faibles pertes requises. Ce mémoire porte essentiellement sur des filtres passe-bande utilisant des tronçons de guides rectangulaires propagatifs et évanescents. L'objectif est d'étudier les performances de filtres rectilignes ou recourbés à bande large ou étroite en bande X et Ku. Le chapitre 1 présente les fondements électromagnétiques et la modélisation, de tronçons de guide chargé par des inserts. Un raccordement modal permet
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Abbas, Boushra. "Méthode de Newton régularisée pour les inclusions monotones structurées : étude des dynamiques et algorithmes associés." Thesis, Montpellier, 2015. http://www.theses.fr/2015MONTS250/document.

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Cette thèse est consacrée à la recherche des zéros d'un opérateur maximal monotone structuré, à l'aide de systèmes dynamiques dissipatifs continus et discrets. Les solutions sont obtenues comme limites des trajectoires lorsque le temps t tend vers l'infini. On s'intéressera principalement aux dynamiques obtenues par régularisation de type Levenberg-Marquardt de la méthode de Newton. On décrira aussi les approches basées sur des dynamiques voisines.Dans un cadre Hilbertien, on s'intéresse à la recherche des zéros de l'opérateur maximal monotone structuré M = A + B, où A est un opérateur maximal
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Book chapters on the topic "Algoritmus Levenberg-Marquardt"

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Prudêncio, Ricardo B. C., and Teresa B. Ludermir. "Neural Network Hybrid Learning: Genetic Algorithms & Levenberg-Marquardt." In Between Data Science and Applied Data Analysis. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-18991-3_53.

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Amin, Md Faijul, Muhammad Ilias Amin, A. Y. H. Al-Nuaimi, and Kazuyuki Murase. "Wirtinger Calculus Based Gradient Descent and Levenberg-Marquardt Learning Algorithms in Complex-Valued Neural Networks." In Neural Information Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24955-6_66.

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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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Aliouane, Leila, Sid-Ali Ouadfeul, Noureddine Djarfour, and Amar Boudella. "Permeability Prediction Using Artificial Neural Networks. A Comparative Study Between Back Propagation and Levenberg–Marquardt Learning Algorithms." In Lecture Notes in Earth System Sciences. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32408-6_142.

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Jazayeri, Kian, Moein Jazayeri, and Sener Uysal. "Comparative Analysis of Levenberg-Marquardt and Bayesian Regularization Backpropagation Algorithms in Photovoltaic Power Estimation Using Artificial Neural Network." In Advances in Data Mining. Applications and Theoretical Aspects. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41561-1_7.

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Yadav, Vibha, and Satyendra Nath. "Novel Application of Linear Scaling to Improve Accuracy of Optimized Artificial Neural Network Using Levenberg-Marquardt Algorithm in Prediction of Daily Nitrogen Oxide for Health Management." In Metaheuristic and Evolutionary Computation: Algorithms and Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7571-6_31.

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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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Sindirgi, Petek, and Şenol Özyalin. "A Comparison of the Model Parameter Estimations from Self-Potential Anomalies by Levenberg-Marquardt (LM), Differential Evolution (DE) and Particle Swarm Optimization (PSO) Algorithms: An Example from Tamış-Çanakkale, Turkey." In Self-Potential Method: Theoretical Modeling and Applications in Geosciences. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79333-3_4.

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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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Conference papers on the topic "Algoritmus Levenberg-Marquardt"

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Popa, Calin-Adrian. "Levenberg-Marquardt Learning Algorithm for Quaternion-Valued Neural Networks." In 2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2016. http://dx.doi.org/10.1109/synasc.2016.050.

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Sunori, Sandeep Kumar, Amit Mittal, Sudhanshu Maurya, et al. "Rainfall Prediction using Subtractive Clustering and Levenberg-Marquardt Algorithms." In 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2021. http://dx.doi.org/10.1109/icoei51242.2021.9452869.

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Wang, Dongqi, Xuanyue Shuai, Xueqiong Hu, and Li Zhu. "Research on Computer Network Security Evaluation Method Based on Levenberg-Marquardt Algorithms." In 2019 International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE, 2019. http://dx.doi.org/10.1109/cisce.2019.00094.

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Lopes, Hudson, and Flávio Rocha. "Aplicação do Algoritmo de Levenberg Marquardt para Modelagem na Alocação de Potência para Usuários LTE na Faixa de 3.5 GHz com Diferentes CQI." In Escola Regional de Informática de Goiás. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/erigo.2020.13863.

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Utilizando o algoritmo de Levenberg-Marquardt (LM) para estimação de parâmetros, apresentamos neste artigo as funções de utilidade sigmoidal que modelam a probabilidade de sucesso por unidade de potência consumida por um usuário. A simulação é proposta em redes móveis para os usuários localizados sob a área de cobertura das pequenas células, com uma faixa de 3,5 GHz e com diferentes Esquemas de Modulação e Codificação (MCS - Modulation and Coding Schemes) padronizados no Projeto de Parceria de Terceira Geração (3GPP - 3rd Generation Partnership Project).
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Kisaki, Mio, Yutaro Yamamura, Hyoungseop Kim, Joo Kooi Tan, Seiji Ishikawa, and Akiyoshi Yamamoto. "High speed image registration of head CT and MR images based on Levenberg-Marquardt algorithms." In 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS). IEEE, 2014. http://dx.doi.org/10.1109/scis-isis.2014.7044694.

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Zhou, Q., W. Yao, W. Wu, X. Li, Z. Zhu, and G. Gildenblat. "Parameter extraction for the PSP MOSFET model by the combination of genetic and Levenberg-Marquardt algorithms." In 2009 IEEE International Conference on Microelectronic Test Structures (ICMTS). IEEE, 2009. http://dx.doi.org/10.1109/icmts.2009.4814627.

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Mohamad, Nadiah, Fatimah Zaini, Aiman Johari, Ihsan Yassin, and Azlee Zabidi. "Comparison between Levenberg-Marquardt and Scaled Conjugate Gradient training algorithms for Breast Cancer Diagnosis using MLP." In its Applications (CSPA). IEEE, 2010. http://dx.doi.org/10.1109/cspa.2010.5545325.

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Valadão, Myke, Waldir Silva, André Costa, et al. "Classificação Automática de Modulações utilizando Redes Neurais Artificiais com regularização Bayesiana e algoritmo de retropropagação de Levenberg-Marquardt." In XXXVIII Simpósio Brasileiro de Telecomunicações e Processamento de Sinais. Sociedade Brasileira de Telecomunicações, 2020. http://dx.doi.org/10.14209/sbrt.2020.1570649633.

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Da Silva, Maykon Renan Pereira, and Flávio Rocha. "Método de Estimação de Parâmetros para Modelagem no Domínio Wavelet do Tráfego de Redes de Computadores Usando o Algoritmo de Levenberg-Marquardt." In Workshop em Desempenho de Sistemas Computacionais e de Comunicação. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wperformance.2020.11104.

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Research has shown that analysis and modeling techniques that provide a better understanding of the behavior of network traffic flows are very important in the design and optimization of communication networks. For this reason, this work proposes a multifractal model based on a multiplicative cascade in the wavelet domain, to synthesize network traffic samples. For this purpose, in the proposed model, a parametric modeling based on an exponential function is used for the variance of the multipliers along the stages of the cascade. The exponential function parameters are obtained through the so
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Soares, Pedro Paulo da Silva, and Jurandir Nadal. "Aplicação de uma Rede Neural Feedforward com Algoritmo de Levenberg-Marquardt para Classificação de Alterações do Segmento ST do Eletrocardiograma." In 4. Congresso Brasileiro de Redes Neurais. CNRN, 2016. http://dx.doi.org/10.21528/cbrn1999-092.

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Reports on the topic "Algoritmus Levenberg-Marquardt"

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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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