Results 211 to 220 of about 63,095 (231)

Modified Levenberg Marquardt Algorithm for Inverse Problems

2010
The Levenberg Marquardt (LM) algorithm is a popular nonlinear least squares optimization technique for solving data matching problems. In this method, the damping parameter plays a vital role in determining the convergence of the system. This damping parameter is calculated arbitrarily in the classical LM, causing it to converge prematurely when used ...
Muthu Naveen, Shankar Jayaraman, Vinay Ramanath, Shamik Chaudhuri   +3 more
openaire   +1 more source

Levenberg-Marquardt method for ANFIS learning

Proceedings of North American Fuzzy Information Processing, 2002
Presents the results of applying the Levenberg-Marquardt method (K. Levenberg, 1944, and D.W. Marquardt, 1963), which is a popular nonlinear least-squares method, to the ANFIS (Adaptive Neuro-Fuzzy Inference System) architecture proposed by Jang (IEEE Trans. on Systems, Man and Cybernctics, vol. 23, no. 3, pp 665-685, May 1993).
null Jyh-Shing Roger Jang, E. Mizutani
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On the Rate of Convergence of the Levenberg-Marquardt Method

2001
We consider a rate of convergence of the Levenberg-Marquardt method (LMM) for solving a system of nonlinear equations F(x) = 0, where F is a mapping from Rn into Rm. It is well-known that LMM has a quadratic rate of convergence when m = n, the Jacobian matrix of F is nonsingular at a solution x and an initial point is chosen sufficiently close to x. In
N. Yamashita, M. Fukushima
openaire   +1 more source

A higher-order Levenberg–Marquardt method for nonlinear equations

Applied Mathematics and Computation, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +2 more sources

On the convergence properties of the Levenberg–Marquardt method

Optimization, 2003
In this article, a new method is presented to update the parameter in the Levenberg–Marquardt Method (LMM) for solving nonlinear equation system, i.e., (namely, there exist positive constants c 2 > 0, c 3 > 0 such that ). The existing methods in [H. Dan, N. Yamashita and M. Fukushima (2001).
openaire   +1 more source

Stability Analysis of the Modified Levenberg–Marquardt Algorithm for the Artificial Neural Network Training

IEEE Transactions on Neural Networks and Learning Systems, 2021
Jose de Jesus Rubio
exaly  

A new approach for determining damping factors in Levenberg-Marquardt algorithm for solving an inverse heat conduction problem

International Journal of Heat and Mass Transfer, 2017
Miao Cui, Xiao-wei Gao
exaly  

On Speeding up the Levenberg-Marquardt Learning Algorithm

2023
Jaroslaw Bilski, Bartosz Kowalczyk, Jacek Smolag   +2 more
openaire   +1 more source

Predictive Abilities of Bayesian Regularization and Levenberg–Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data

Mathematical and Computational Applications, 2016
Murat Kayri
exaly  

A Shamanskii-like self-adaptive Levenberg–Marquardt method for nonlinear equations

Computers and Mathematics With Applications, 2019
Haohua Huang, Chang-Feng
exaly