• Journal of the Korean GEO-environmental Society
• /
• v.10 no.5
• /
• pp.49-57
• /
• 2009

In this study, one of the complicated geotechnical problem, compression index was predicted by a artificial neural network method of Levenberg-Marquardt (LM) algorithm. Predicted values were compared and evaluated by the results of the Back Propagation (BP) method, which is used extensively in geotechnical engineering. Also two different results were compared with experimental values estimated by verified experimental methods in order to evaluate the accuracy of each method. The results from experimental method generally showed higher error than the results of both artificial neural network method. The predicted compression index by LM algorithm showed better comprehensive results than BP algorithm in terms of convergence, but accuracy was similar each other.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.52 no.8
• /
• pp.148-155
• /
• 2015

The Levenberg-Marquardt method is a well known solution about the least square problem. However, in a Target Motion Analysis(TMA) application most of researches have used the Gauss-Newton method as a batch estimator, which of inverse matrix calculation may causes instability problem. In this paper, Levenberg-Marquardt method is applied to TMA problem to prevent its divergence. In experiment, its performance is compared with Gauss-Newton in domain of range, course and speed. Monte Carlo simulation reveals the convergence time and reliability of the TMA based on Levenberg-Marquardt.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.8
• /
• pp.11-18
• /
• 2009

This paper proposes the way of improving learning speed in Levenberg-Marquardt algorithm using the principal submatrix of Jacobian matrix. The Levenberg-Marquardt learning uses Jacobian matrix for Hessian matrix to get the second derivative of an error function. To make the Jacobian matrix an invertible matrix. the Levenberg-Marquardt learning must increase or decrease ${\mu}$ and recalculate the inverse matrix of the Jacobian matrix due to these changes of ${\mu}$. Therefore, to have the proper ${\mu}$, we create the principal submatrix of Jacobian matrix and set the ${\mu}$ as the eigenvalues sum of the principal submatrix. which can make learning speed improve without calculating an additional inverse matrix. We also showed that our method was able to improve learning speed in both a generalized XOR problem and a handwritten digit recognition problem.

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.4
• /
• pp.57-63
• /
• 2010

The damping parameter of Levenberg-Marquardt algorithm switches between error backpropagation and Gauss-Newton learning and affects learning speed. Fixing the damping parameter induces some oscillation of error and decreases learning speed. Therefore, we propose the way of a variable damping parameter with referring to the alternation of error. The proposed method makes the damping parameter increase if error rate is large and makes it decrease if error rate is small. This method so plays the role of momentum that it can improve learning speed. We tested both iris recognition and wine recognition for this paper. We found out that this method improved learning speed in 67% cases on iris recognition and in 78% cases on wine recognition. It was also showed that the oscillation of error by the proposed way was less than those of other algorithms.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.14 no.8
• /
• pp.803-808
• /
• 2003

Several dielectric pipes buried in the lossy half space are reconstructed from the scattered fields measured along the interface between the air and the lossy ground. Iterative inversion method by using the hybrid optimization algorithm combining the genetic and the Levenberg-Marquardt algorithm enables us to find the positions, the sizes, and the medium parameters such as the permittivities and the conductivities of the buried pipes as well as those of the background lossy half space even when the dielectric pipes are close together. Illposedness of the inversion caused by the errors in the measured scattered fields are regularized by filtering the evanescent modes of the scattered fields out.

• Smart Media Journal
• /
• v.3 no.2
• /
• pp.9-13
• /
• 2014

The Levenberg-Marquardt (LM) algorithm is the most widely used fitting algorithm. It outperforms simple gradient descent and other conjugate gradient methods in a wide variety of problems. Based on the work of paper[1], we propose a modified Levenberg-Marquardt algorithm for better performance of mobile system. The LM parameter at the $k_{th}$ iteration is chosen ${\mu}=A{\bullet}{\parallel}f(x){\parallel}{\bullet}I$ where f is the residual function, and J is the Jacobi of f. In this paper, we show this method is more efficient than traditional method under the situation that the system iteration is limited.

• Proceedings of the KIEE Conference
• /
• 2001.11c
• /
• pp.204-208
• /
• 2001

In this paper, we induce the linear transfer function of Downcomer water level of NPP(Nuclear Power Plant) Steam Generator using Genetic Algorithm and Levenberg-Marquardt Algorithm. The characteristic of NPP S/G mechanism is so high-non-linear that it is hard to achieve mathematical expression. So we use non-mathematical Algorithms to get the model function of NPP S/G water level. S/G level controller would be designed with this transfer function as the plant.

• Journal of Korea Water Resources Association
• /
• v.46 no.4
• /
• pp.389-400
• /
• 2013

In this study, a conventional pipeline unsteady friction model has been integrated into Levenberg Marquardt method to calibrate friction coefficient in a pipeline system. The method of characteristics has been employed as the modeling platform for the frequency dependant model of unsteady friction. In order to obtain Hessian and Jacobian matrix for optimization, the direct differentiation of pressure to friction factor was calculated and sensitivities to friction for heads and discharges were formulated for implementation to the integration constant in the characteristic method. Using a hypothetical simple pipeline system, time series of pressure, introduced by a sudden valve closure, were obtained for various Reynolds numbers. Convergency in fiction factors were evaluated both in steady and unsteady friction models. The comparison of calibration performance between the proposed method and genetic algorithm indicates that faster and stabler behaviour of Levenberg Marquardt method than those of evolutionary calibration.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.8 no.5
• /
• pp.534-544
• /
• 1997

The permittivity distribution of a two-dimensional high-contrast object with large size, which leads to the global minimum of cost function, is reconstructed by iteratively using the hybrid algorithm of Levenberg-magquardt algorithm(LMA) plus Genetic Algorithm(GA). The scattered fields calculated in a cost function are expanded in angular spectral modes, of which only effective propagating modes are used. The definition of cost function based on the effective propagating modes enables us to formulate the minimum number of incident waves for the reconstruction of object. It is numerically shown that LMA has an advantage of fast convergence but can't reconstruct a high-contrast object with large size and GA can reconstruct a high-contrast object with large size but has an disadvantage of slow convergence, whereas an inverse scattering technique using the hybrid algorithm adopts only advantages of LMA and GA.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.16 no.2
• /
• pp.115-124
• /
• 2003

In this paper, the effects of vehicle loads on flat slab system are investigated on the basis of the previous studies for beam-gilder parking structural system. The influence surfaces of flat slab for a typical design section are constructed lot the purpose of obtaining maximum member forces under vehicle loads. In addition, the equivalent vehicle load factors for flat slab parking structures are suggested using artificial neural network. The network responses we compared with the results obtained by numerical analyses to verify the validation of Levenberg-Marquardt algorithm adopted as training method in this Paper. Many parameter studies for the flat slab structural system show dominant vehicle load effects at the center positive moments in both column and middle strips, like the beam-girder parking structural system.