Results 181 to 190 of about 54,415 (225)
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Nonmonotone Levenberg–Marquardt Algorithms and Their Convergence Analysis
Journal of Optimization Theory and Applications, 1997zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhang, J. Z., Chen, L. H.
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LMA: A generic and efficient implementation of the Levenberg–Marquardt Algorithm
Software: Practice and Experience, 2017SummaryThis paper presents an open‐source, generic and efficient implementation of a very popular nonlinear optimization method: the Levenberg–Marquardt algorithm (LMA). This minimization algorithm is well known and hundreds of implementations have already been released.
Ramadasan, Datta +2 more
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A smoothing Levenberg–Marquardt algorithm for semi-infinite programming
Computational Optimization and Applications, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ping Jin, Chen Ling 0001, Huifei Shen
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The Parallel Modification to the Levenberg-Marquardt Algorithm
2018The paper presents a parallel approach to the Levenberg-Marquardt algorithm (also called LM or LMA). The first section contains the mathematical basics of the classic LMA. Then the parallel modification to LMA is introduced. The classic Levenberg-Marquardt algorithm is sufficient for a training of small neural networks.
Jaroslaw Bilski +2 more
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Levenberg-Marquardt iterative algorithm for Hammerstein nonlinear systems
2015 International Conference on Control, Automation and Information Sciences (ICCAIS), 2015In this paper, we study the identification problem of Hammerstein nonlinear systems. A Levenberg-Marquardt iterative (LMI) algorithm is developed for Hammerstein nonlinear systems. The basic idea is to establish a Hammerstein nonlinear model by means of the key-term separation principle and then derive the LMI algorithm by replacing the unmeasurable ...
Lincheng Zhou +3 more
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Parallel and separable recursive Levenberg-Marquardt training algorithm
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing, 2003A novel decomposed recursive Levenberg Marquardt (RLM) algorithm is derived for the training of feedforward neural networks. By neglecting interneuron weight correlations the recently proposed RLM training algorithm can be decomposed at neuron level enabling weights to be updated in an efficient parallel manner. A separable least squares implementation
Vijanth S. Asirvadam +2 more
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Modified Levenberg Marquardt Algorithm for Inverse Problems
2010The 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 +3 more
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A sliding window solution for the on-line implementation of the Levenberg–Marquardt algorithm
Engineering Applications of Artificial Intelligence, 2006The Levenberg-Marquardt algorithm is considered as the most effective one for training artificial neural networks but its computational complexity and the difficulty to compute the trust region have made it very difficult to develop a true iterative version to use in on-line training.
Fernando Morgado-Dias, Alexandre Mota
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A new robust correntropy based Levenberg-Marquardt algorithm
2016 Iran Workshop on Communication and Information Theory (IWCIT), 2016Recently, there has been much interest in information theoretic learning (ITL) criteria, widely used in several applications with different robust algorithms. Dealing with the rate convergence of the information theoretic robust approaches, we introduce a fast novel correntropy based algorithm, as correntropy based Levenberg-Marquardt (CLM), and apply ...
Ahmad Reza Heravi, Ghosheh Abed Hodtani
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Convergence analysis of nonmonotone Levenberg–Marquardt algorithms for complementarity problem
Applied Mathematics and Computation, 2010The convergence of two nonmonotone Levenberg-Marquardt algorithms for nonlinear complementarity problem is studied and proved. Under some mild assumptions, and requiring only the solution of a linear system at each iteration, the proposed nonmonotone Levenberg-Marquardt algorithms are shown to be globally convergent.
Shou-qiang Du, Yan Gao
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