Vehicle Recognition System Using Singular Value Decomposition (SVD) and Levenberg-Marquardt

2009 ◽  
Author(s):  
Zuraidi Saad ◽  
Muhammad Khusairi Osman ◽  
Zuli Imran Zulkafli ◽  
Sopiah Ishak
Keyword(s):  
Singular Value Decomposition ◽  
Recognition System ◽  
Singular Value ◽  
Vehicle Recognition ◽  
Levenberg Marquardt ◽  
Value Decomposition

Structural Physical Parameter Identification of Nonlinear Parametric System in Time Domain

2011 ◽  
Vol 94-96 ◽  
pp. 1040-1043
Author(s):  
Xiang Jian Wang ◽  
Jie Cui
Keyword(s):  
Singular Value Decomposition ◽  
Parameter Identification ◽  
Physical Parameter ◽  
High Precision ◽  
Time Domain ◽  
Singular Value ◽  
Parametric System ◽  
Levenberg Marquardt ◽  
Shear Type ◽  
Value Decomposition

The modified Levenberg-Marquardt (mLM) method is introduced for nonlinear parametric system, such as stiffness proportional damping and Rayleigh proportional damping. Since the mLM method is sensitive to the initial values of parameter, a SVD-mLM method is proposed with combination of singular value decomposition (SVD). Parameter identification of five-storey shear-type is simulated with incomplete output. The results show that the identified parameters have high precision, and the proposed method is effective and robust on noise.

scholarly journals Improving Levenberg-Marquardt Algorithm Inversion Result Using Singular Value Decomposition

2016 ◽  
Vol 5 (2) ◽  
pp. 20
Author(s):  
Widodo Widodo ◽  
Durra Handri Saputera
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Parameter Analysis ◽  
Model Parameters ◽  
Inversion Result ◽  
One Dimensional ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Value Decomposition ◽  
Lm Algorithm

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 algorithm without SVD in order to understand how much it has been improved. To simplify the comparison, simple synthetic model is used in this study. From this study, the new algorithm can improve the result of the original LM algorithm. In addition, SVD is allowing more parameter analysis to be done in its process. The algorithm created from this study is then used in our modeling program, called MAT1DMT.

scholarly journals Petrophysical parameters estimation using Levenberg-Marquardt and Singular Value Decomposition inversion schemes

2021 ◽  
Vol 11 (5) ◽  
pp. 24-38
Author(s):  
Moataz Mohamed Gomaa Abdelrahman ◽  
Norbert Péter Szabó ◽  
Mihály Dobróka
Keyword(s):  
Singular Value Decomposition ◽  
Synthetic Data ◽  
Data Interpretation ◽  
Singular Value ◽  
Parameters Estimation ◽  
Petrophysical Parameters ◽  
Inversion Methods ◽  
Levenberg Marquardt ◽  
Robustness To Noise ◽  
Value Decomposition

Well logging inversion was carried out using Levenberg-Marquardt (LM) and Singular Value Decomposition (SVD) techniques for the determination of petrophysical parameters, respectively. In this research, synthetic data contaminated with 5% Gaussian noise, and field data were used to compare the results from the two inversion methods. MATLAB software has been developed to solve the overdetermined inverse problem. The estimated petrophysical parameters from both inversion methods had been compared to one another in terms of robustness to noise, rock interface differentiation, different fluid prediction, and the accuracy of the estimated parameters. This research returns the reason to the inner iterative loop which is considered more about the Jacobian matrix sensitivity. The inversion results showed that both methods can be used in petrophysical data estimation for a reliable well-log data interpretation.

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