Results 191 to 200 of about 62,442 (223)
ANN-based thermal analysis of 3D MHD hybrid nanofluid flow over a shrinking sheet via LMA. [PDF]
Sci RepImran M +5 more
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Improved Kalman Filtering Algorithm Based on Levenberg-Marquart Algorithm in Ultra-Wideband Indoor Positioning. [PDF]
Sensors (Basel)Xie C, Fang X, Yang X.
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Simplified Levenberg–Marquardt Method in Hilbert Spaces
Computational Methods in Applied Mathematics, 2022Abstract In 2010, Qinian Jin considered a regularized Levenberg–Marquardt method in Hilbert spaces for getting stable approximate solution for nonlinear ill-posed operator equation F
Mahale, Pallavi, Shaikh, Farheen M.
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Improved Computation for Levenberg–Marquardt Training
IEEE Transactions on Neural Networks, 2010The improved computation presented in this paper is aimed to optimize the neural networks learning process using Levenberg-Marquardt (LM) algorithm. Quasi-Hessian matrix and gradient vector are computed directly, without Jacobian matrix multiplication and storage. The memory limitation problem for LM training is solved.
Bogdan M, Wilamowski, Hao, Yu
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A parallel levenberg-marquardt algorithm
Proceedings of the 23rd international conference on Supercomputing, 2009This paper describes a parallel Levenberg-Marquardt algorithm that has been implemented as part of a larger system to support the kinetic modeling of polymer chemistry. The Levenberg-Marquardt algorithm finds a local minimum of a function by varying parameters of the function.
Cao, Jun +4 more
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Geometric Algebra Levenberg-Marquardt
2019This paper introduces a novel and matrix-free implementation of the widely used Levenberg-Marquardt algorithm, in the language of Geometric Algebra. The resulting algorithm is shown to be compact, geometrically intuitive, numerically stable and well suited for efficient GPU implementation.
De Keninck, S., Dorst, L.
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An Adaptive Multi-step Levenberg–Marquardt Method
Journal of Scientific Computing, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fan, Jinyan +2 more
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A note on the Levenberg–Marquardt parameter
Applied Mathematics and Computation, 2009The authors consider the problem of determining efficient Levenberg-Marquardt (LM) parameters for systems of nonlinear equations (1) \(F(x)= 0\), where \(F: \mathbb{R}^n\to\mathbb{R}^n\) is a continuously differentiable function, provided \(\| F(x)\|\) satisfies a local error bound condition which is weaker than nonsingularity.
Fan, Jinyan, Pan, Jianyu
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A smoothing Levenberg–Marquardt method for NCP
Applied Mathematics and Computation, 2006Nonlinear complementarity problems (NCPs) are converted to an equivalent system of smooth nonlinear equations by using a smoothing technique. Then a Levenberg-Marquardt type method is used to solve the system of nonlinear equations. The method has the following merits: (i) any cluster point of the iteration sequence is a solution of the \(P_{0}\)-NCP; (
Zhang, Ju-Liang, Zhang, Xiangsun
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2011
This chapter introduces the implementation of training with the Levenberg–Marquardt algorithm in two parts: calculation of the Jacobian matrix and training process design. The Levenberg–Marquardt algorithm, which was independently developed by Kenneth Levenberg and Donald Marquardt, provides a numerical solution to the problem of minimizing a nonlinear
Hao Yu, Bogdan M. Wilamowski
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This chapter introduces the implementation of training with the Levenberg–Marquardt algorithm in two parts: calculation of the Jacobian matrix and training process design. The Levenberg–Marquardt algorithm, which was independently developed by Kenneth Levenberg and Donald Marquardt, provides a numerical solution to the problem of minimizing a nonlinear
Hao Yu, Bogdan M. Wilamowski
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