Relevant bibliographies by topics / Levenberg-Marquard method

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Academic literature on the topic 'Levenberg-Marquard method'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Levenberg-Marquard method"

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

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Cylindrical uranium dioxide pellets, which are the main components for nuclear fuel elements in Light Water Reactor, should have a high density profile, uniform shape and quality for the safety used as a reactor fuel component. The quality of green pellets is conventionally monitored through a laboratory measurement of the physical pellets characteristics followed by a graphical chart classification technique. However, this conventional classification method shows some drawbacks, such as the difficulties on its usage, low accuracy and time consuming, and does not have the ability to adress the non-linearity and the complexity of the relationship between the pellet’s quality variables and the pellett’s quality. In this paper, an Improved Levenberg-Marquard based neural networks is used to classify the quality process of the green pellets. Robustness of this learning algorithm is evaluated by comparing its recognition rate to that of the conventional Back Propagation neural learning algorithm. Results show that the Improved Levenberg-Marquard algorithm outperformed the Back Propagation learning algorthm for various percentage of training/testing paradigm, showing that this system could be applied effectively for classification of pellet quality.
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Chen, Bing, Gang Lu, Hongzhen Fang, Li Ai, and Huanzhen Fan. "IGBT Neural Network Prediction Method of Radar Transmitter based on Levenberg-Marquard Optimization." International Journal of Future Generation Communication and Networking 9, no. 9 (September 30, 2016): 1–14. http://dx.doi.org/10.14257/ijfgcn.2016.9.9.01.

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Zhang, Ka, Xiao Sheng Cheng, Ning Dai, and Hai Hua Cui. "Optimization Design of the Secondary Calibration of Line Structured Light Vision System." Applied Mechanics and Materials 37-38 (November 2010): 797–800. http://dx.doi.org/10.4028/www.scientific.net/amm.37-38.797.

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As the traditional calibration methods are difficult to ensure the calibration accuracy and system stability, a new optimization design of the secondary calibration based on Levenberg- Marquard (LM) algorithm is proposed. It is the optimization and correction of the first calibration results. The method introduces the optimization idea of the LM algorithm. The required objective function can be obtained by capturing the image of standard block after the first system calibration. The problem is solved satisfactorily with the optimization theory of LM algorithm. Experimental results show the optimized system accuracy is less than 0.01mm, RMS error is less than 0.02mm. The proposed algorithm has significant effect on accuracy improvement, and the system is more stable.
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Wu, Shunxing, Hong-Zhi Yan, Rengui Bi, Zhiyong Wang, and Pengfei Zhu. "Nonlinear Optimization Method for Transmission Error of Hypoid Gear Machined by the Duplex Helical Method." Mathematical Problems in Engineering 2020 (November 27, 2020): 1–15. http://dx.doi.org/10.1155/2020/9626089.

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In this study, synchronous cutting of concave and convex surfaces for hypoid gear was achieved using a duplex helical method. Precise, nonlinear optimization of the transmission error driven by machine tool parameters was performed to reduce the vibration noise of the gear pair. First, the transmission error curve and contact path of the tooth surface of the initial pinion were solved using tooth contact analysis. Second, according to the preset parabolic transmission error curve, the initial gear was used to generate the target pinion, which coincided with the contact path of the initial pinion. Finally, a deviation correction model of the discrete points, corresponding to the contact paths on the concave and convex surfaces of the target and initial pinions, was established. This model was solved using the Levenberg–Marquard algorithm with the trust region strategy, to obtain optimized machine tool parameters. Synchronous optimization of the transmission errors of concave and convex surfaces of the pinion was achieved by correcting the deviations of the contact points. The effectiveness of the proposed method was verified by a numerical example and by performing a contact area rolling test.
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Liu, Z., X. Gao, G. Li, and J. Chen. "DECOMPOSITION TECHNIQUES FOR ICESAT/GLAS FULL-WAVEFORM DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1179–82. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1179-2018.

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The geoscience laser altimeter system (GLAS) on the board Ice, Cloud, and land Elevation Satellite (ICESat), is the first long-duration space borne full-waveform LiDAR for measuring the topography of the ice shelf and temporal variation, cloud and atmospheric characteristics. In order to extract the characteristic parameters of the waveform, the key step is to process the full waveform data. In this paper, the modified waveform decomposition method is proposed to extract the echo components from full-waveform. First, the initial parameter estimation is implemented through data preprocessing and waveform detection. Next, the waveform fitting is demonstrated using the Levenberg-Marquard (LM) optimization method. The results show that the modified waveform decomposition method can effectively extract the overlapped echo components and missing echo components compared with the results from GLA14 product. The echo components can also be extracted from the complex waveforms.
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Garbaa, Hela, Lidia Jackowska-Strumiłło, Krzysztof Grudzień, and Andrzej Romanowski. "Application of electrical capacitance tomography and artificial neural networks to rapid estimation of cylindrical shape parameters of industrial flow structure." Archives of Electrical Engineering 65, no. 4 (December 1, 2016): 657–69. http://dx.doi.org/10.1515/aee-2016-0046.

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Abstract A new approach to solve the inverse problem in electrical capacitance tomography is presented. The proposed method is based on an artificial neural network to estimate three different parameters of a circular object present inside a pipeline, i.e. radius and 2D position coordinates. This information allows the estimation of the distribution of material inside a pipe and determination of the characteristic parameters of a range of flows, which are characterised by a circular objects emerging within a cross section such as funnel flow in a silo gravitational discharging process. The main advantages of the proposed approach are explicitly: the desired characteristic flow parameters are estimated directly from the measured capacitances and rapidity, which in turn is crucial for online flow monitoring. In a classic approach in order to obtain these parameters in the first step the image is reconstructed and then the parameters are estimated with the use of image processing methods. The obtained results showed significant reduction of computations time in comparison to the iterative LBP or Levenberg-Marquard algorithms.
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Ghobadi, Ehsan, Alexey Shutov, and Holger Steeb. "Parameter Identification and Validation of Shape-Memory Polymers within the Framework of Finite Strain Viscoelasticity." Materials 14, no. 8 (April 19, 2021): 2049. http://dx.doi.org/10.3390/ma14082049.

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Shape-Memory Polymers (SMPs) can be stretched to large deformations and recover induced strains when exposed to an appropriate stimulus, such as heat. This emerging class of functional polymers has attracted much interest and found applications in medicine and engineering. Nevertheless, prior to any application, their physical and mechanical properties must be thoroughly studied and understood in order to make predictions or to design structures thereof. In this contribution, the viscoelastic behavior of a polyether-based polyurethane (Estane) and its rate- and temperature-dependent behavior have been studied experimentally and by the mean of simulations. The model-inherent material parameters are identified with the assumption of the thermo-rheological complexity. Here, the numerical results of uni-axial stress relaxations were compared with the associated experiments in conjucation with the Levenberg-Marquard optimization method to determine the parameters of the Prony equation. The ability of the model to simulate the thermo-mechanical properties of Estane was evaluated by data-rich experimental observations on tension and torsion in various temperature ranges. Heterogeneous tests are included into the experimental program to cover a broader spectrum of loading scenarios.
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Bakurova, Anna, Olesia Yuskiv, Dima Shyrokorad, Anton Riabenko, and Elina Tereschenko. "NEURAL NETWORK FORECASTING OF ENERGY CONSUMPTION OF A METALLURGICAL ENTERPRISE." Innovative Technologies and Scientific Solutions for Industries, no. 1 (15) (March 31, 2021): 14–22. http://dx.doi.org/10.30837/itssi.2021.15.014.

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The subject of the research is the methods of constructing and training neural networks as a nonlinear modeling apparatus for solving the problem of predicting the energy consumption of metallurgical enterprises. The purpose of this work is to develop a model for forecasting the consumption of the power system of a metallurgical enterprise and its experimental testing on the data available for research of PJSC "Dneprospetsstal". The following tasks have been solved: analysis of the time series of power consumption; building a model with the help of which data on electricity consumption for a historical period is processed; building the most accurate forecast of the actual amount of electricity for the day ahead; assessment of the forecast quality. Methods used: time series analysis, neural network modeling, short-term forecasting of energy consumption in the metallurgical industry. The results obtained: to develop a model for predicting the energy consumption of a metallurgical enterprise based on artificial neural networks, the MATLAB complex with the Neural Network Toolbox was chosen. When conducting experiments, based on the available statistical data of a metallurgical enterprise, a selection of architectures and algorithms for learning neural networks was carried out. The best results were shown by the feedforward and backpropagation network, architecture with nonlinear autoregressive and learning algorithms: Levenberg-Marquard nonlinear optimization, Bayesian Regularization method and conjugate gradient method. Another approach, deep learning, is also considered, namely the neural network with long short-term memory LSTM and the adam learning algorithm. Such a deep neural network allows you to process large amounts of input information in a short time and build dependencies with uninformative input information. The LSTM network turned out to be the most effective among the considered neural networks, for which the indicator of the maximum prediction error had the minimum value. Conclusions: analysis of forecasting results using the developed models showed that the chosen approach with experimentally selected architectures and learning algorithms meets the necessary requirements for forecast accuracy when developing a forecasting model based on artificial neural networks. The use of models will allow automating high-precision operational hourly forecasting of energy consumption in market conditions. Keywords: energy consumption; forecasting; artificial neural network; time series.
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Jalid, Abdelilah, Said Hariri, and Jean Paul Senelaer. "Estimation of form deviation and the associated uncertainty in coordinate metrology." International Journal of Quality & Reliability Management 32, no. 5 (May 5, 2015): 456–71. http://dx.doi.org/10.1108/ijqrm-06-2012-0087.

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Purpose – The uncertainty evaluation for coordinate measuring machine metrology is problematic due to the diversity of the parameters that can influence the measurement result. From discrete coordinate data taken on curve (or surface) the software of these machines proceeds to an identification of the measured feature, the parameters of the substitute feature serve in the phase of calculation to estimate the form error of form, and the decisions made based on the result measurement may be outliers when the uncertainty associated to the measurement result is not taken into account. The paper aims to discuss these issues. Design/methodology/approach – The authors relied on the orthogonal distance regression (ODR) algorithm to estimate the parameters of the substitute geometrical elements and their uncertainties. The solution of the problem is resolved by an iterative calculation according to the Levenberg Marquard optimization method. The authors have also presented in this paper the propagation model of uncertainties to the circularity error. This model is based on the law of propagation of the uncertainties defined in the GUM. Findings – This work proposes a model based on ODR to estimates parameters of the substitute geometrical elements and their uncertainties. This contribution allows us to estimate the uncertaintof the form error by applying the law of propagation of uncertainties. An example of calculating the circularity error and the associated uncertainty is explained. This method can be applied to others geometry type: line, plan, sphere, cylinder and cone. Practical implications – This work interested manufacturing firms by allowing them: to meet the normative, which requires that each measurement must be accompanied by its uncertainty-in conformity assessment, the decision-making must take account of this uncertainty to avoid the aberrant decisions. Informing the operators on the capability of their measurement process Originality/value – This work proposes a model based on ODR to estimates parameters of the substitute geometrical elements and its uncertainties. without the hypothesis of small displacements torsor, this method integrates the uncertainty on the coordinates of points and can be applied in any reference placemark. This contribution allows us also to estimate the uncertainty of the form error by applying the law of propagation of uncertainties.
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Pang, Xinfu, Yang Yu, Haibo Li, Yuan Wang, and Jinhui Zhao. "Estimation of Heat Flux in Two-Dimensional Nonhomogeneous Parabolic Equation Based on a Sufficient Descent Levenberg–Marquard Algorithm." Journal of Mathematics 2021 (May 27, 2021): 1–15. http://dx.doi.org/10.1155/2021/6616326.

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The main work of this paper focuses on identifying the heat flux in inverse problem of two-dimensional nonhomogeneous parabolic equation, which has wide application in the industrial field such as steel-making and continuous casting. Firstly, the existence of the weak solution of the inverse problem is discussed. With the help of forward solution and dual equation, this paper proves the Lipchitz continuity of the cost function and derives the Lipchitz constant. Furthermore, in order to accelerate the convergence rate and reduce the running time, this paper presents a sufficient descent Levenberg–Marquard algorithm with adaptive parameter (SD-LMAP) to solve this inverse problem. At last, compared with other methods, the results of simulation experiment show that this algorithm can obviously reduce the running time and iterative number.
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Dissertations / Theses on the topic "Levenberg-Marquard method"

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Špérová, Alice. "Výpočet oteplení elektrických točivých strojů metodou tepelných sítí." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-233457.

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Práce se zabývá konstrukcí a využitím tepelných sítí k simulaci oteplení elektrických motorů velkých výkonú. Po úvodu do termiky a teorie proudění je popsána konstrukce tří různých typů sítí pro odlišné ventilační schémata motoru. Dále jsou popsány optimalizační metody a jejich možnosti k tepelné optimalizaci motorů. Je vybrána a vysvělena Marquard-Levenbergova metoda a na konkrétním případě je vysvětleno její použití a výhody pro optimalizaci chlazení elektrického motoru. V závěru se práce zabývá také citlivostí teplotních sití na jednotlivé vstupní parametry, porovnáním simulací s měřenými výsledky a také vlivem teplotních závislosti jenotlivých prvků sítě.
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Conference papers on the topic "Levenberg-Marquard method"

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Erturk, Hakan. "Non-Destructive Characterization of Multi Layer Objects by Thermal Tomography." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-12787.

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Multi layered systems are frequently used for thermal management where the quality of the layers and the interfaces are critical in achieving desired operating conditions. Defects that introduce an additional thermal resistance in the thermal path must be prevented. Non-destructive characterization tools such as computerized tomography or scanning acoustic microscopy have been used to identify such problems, and help improve manufacturing process to ensure product quality. In a system with opaque solids, thermal tomography that relies on a thermal signal diffusing through the layers can be used to identify the defects in the system. Such a technique can constitute a practical and low cost alternative to the other non-destructive testing methods necessitating expansive equipment. Feasibility of thermal tomography for characterization of multilayer systems is tested considering different measurement configurations based on areal and discrete measurements. The configurations are compared considering different geometric parameters with Levenberg-Marquard method used for image reconstruction.
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Erturk, Hakan. "Characterization of Electronic Packages by Thermal Diffusion Tomography." In ASME 2009 Heat Transfer Summer Conference collocated with the InterPACK09 and 3rd Energy Sustainability Conferences. ASMEDC, 2009. http://dx.doi.org/10.1115/ht2009-88380.

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One of the most important functions of an electronic package is thermal management, as package is responsible from removing the heat generated by the transistors to ensure reliability. The quality of the package is very important for proper thermal management and it is important to have minimal flaws that increase thermal resistance of the package. Therefore, detection of flaws in the multi-layered package is critical during the assembly process development to monitor the package quality. This is achieved by techniques such as computerized tomography (CT) using x-rays, or scanning acoustic microscopy (SAM), all of which require very expensive equipment and significant processing time. Thermal diffusion tomography (TDT) can be used for detecting the flaws as a lower cost alternative to these imaging techniques. The feasibility of TDT as a fault detection technique for electronic packages with IR thermometry is considered in the current study. Two reconstruction algorithms considered; an iterative perturbation approach and Levenberg-Marquard method were found to be capable of detecting the flaws in the thermal interface layer.
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