• Computers and Concrete
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• v.32 no.4
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• pp.399-410
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• 2023

This study proposes a novel use of backpropagated Levenberg-Marquardt neural networks based on computational intelligence heuristics to comprehend the examination of hybrid nanoparticles on the flow of dusty liquid via stretched cylinder. A two-phase model is employed in the present work to describe the fluid flow. The use of desulphated nanoparticles of silver and molybdenum suspended in water as base fluid. The mathematical model represented in terms of partial differential equations, Implementing similarity transformationsis model is converted to ordinary differential equations for the analysis . By adjusting the particle mass concentration and curvature parameter, a unique technique is utilized to generate a dataset for the proposed Levenberg-Marquardt neural networks in various nanoparticle circumstances on the flow of dusty liquid via stretched cylinder. The intelligent solver Levenberg-Marquardt neural networks is trained, tested and verified to identify the nanoparticles on the flow of dusty liquid solution for various situations. The Levenberg-Marquardt neural networks approach is applied for the solution of the hybrid nanoparticles on the flow of dusty liquid via stretched cylinder model. It is validated by comparison with the standard solution, regression analysis, histograms, and absolute error analysis. Strong agreement between proposed results and reference solutions as well as accuracy provide an evidence of the framework's validity.

• Geophysics and Geophysical Exploration
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• v.22 no.4
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• pp.195-201
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• 2019

The logarithmic objective function used in waveform inversion minimizes the logarithmic differences between the observed and modeled data. Laplace-domain waveform inversions usually adopt the logarithmic objective function and the diagonal elements of the pseudo-Hessian for optimization. In this case, we apply the Levenberg-Marquardt algorithm to prevent the diagonal elements of the pseudo-Hessian from being zero or near-zero values. In this study, we analyzed the diagonal elements of the pseudo-Hessian of the logarithmic objective function and showed that there is no zero or near-zero value in the diagonal elements of the pseudo-Hessian for acoustic waveform inversion in the Laplace domain. Accordingly, we do not need to apply the Levenberg-Marquardt algorithm when we regularize the gradient direction using the pseudo-Hessian of the logarithmic objective function. Numerical examples using synthetic and field datasets demonstrate that we can obtain inversion results without applying the Levenberg-Marquardt method.

• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.1027-1035
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• 2012

This paper proposes a new Levenberg-Marquardt algorithm that is accelerated by adjusting a Jacobian matrix and a quasi-Hessian matrix. The proposed method partitions the Jacobian matrix into block matrices and employs the inverse of a partitioned matrix to find the inverse of the quasi-Hessian matrix. Our method can avoid expensive operations and save memory in calculating the inverse of the quasi-Hessian matrix. It can shorten the training time for fast convergence. In our results tested in a large application, we were able to save about 20% of the training time than other algorithms.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.23 no.3
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• pp.364-373
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• 1999

A combination of conjugate gradient and Levenberg-Marquardt method is used to estimate the spatially varying scattering coefficient, ${\sigma}(r)$, in the solid and hollow spheres by utilizing the measured transmitted beams from the solution of an inverse analysis. The direct radiation problem associated with the inverse problem is solved by using the $S_{12}-approximation$ of the discrete ordinates method. The accuracy of the computations increased when the results from the conjugate gradient method are used as an initial guess for the Levenberg-Marquardt method of minimization. Optical thickness up to ${\tau}_0=3$ is used for the computations. Three different values of standard deviation are considered to examine the accuracy of the solution from the inverse analysis.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.204-208
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• 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 the Korean Society for Precision Engineering
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• v.20 no.2
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• pp.184-191
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• 2003

A parallel manipulator has high stiffness and all the joint errors on the device are not accumulated at the end -effector unlike a serial manipulator. These are the reasons why the parallel manipulator has been widely used in many fields of industry. In the parallel manipulator, it is very important to predict the exact pose of the end-effector when we want to control the end-effector motion. Installation errors have to be determined in order to predict and control the actual position and pose of the end-effector. This paper presents an algorithm to find the whole 36 joint error components with joint clearance errors and measurement errors considered, when a link length measurement sensor is used and data more than 36 times are acquired for 36 different configurations. A simulation test using this algorithm is performed with a Matlab program which uses the Levenberg-Marquardt method that is known to be efficient for non-linear optimization.

• Journal of information and communication convergence engineering
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• v.2 no.3
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• pp.177-181
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• 2004

In this paper, we suggest an Iris recognition system with an excellent recognition rate and confidence as an alternative biometric recognition technique that solves the limit in an existing individual discrimination. For its implementation, we extracted coefficients feature values with the wavelet transformation mainly used in the signal processing, and we used neural network to see a recognition rate. However, Scale Conjugate Gradient of nonlinear optimum method mainly used in neural network is not suitable to solve the optimum problem for its slow velocity of convergence. So we intended to enhance the recognition rate by using Levenberg-Marquardt Back-propagation which supplements existing Scale Conjugate Gradient for an implementation of the iris recognition system. We improved convergence velocity, efficiency, and stability by changing properly the size according to both convergence rate of solution and variation rate of variable vector with the implementation of an applied algorithm.

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

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

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.9
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• pp.1769-1774
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• 2009

An algorithm for extracting the BJT DC model parameter values for SPICE model is proposed. The nonlinear optimization method for analyzing the device I-V data using the Levenberg-Marquardt algorithm is proposed and the method for calculating initial conditions of model parameters to improve the convergence characteristics is proposed. The base current and collector current obtained from the proposed method shows the root mean square error of 6.04% compared with the measured data of the PNP BJT named 2SA1980.