Results 191 to 200 of about 54,415 (225)
Some of the next articles are maybe not open access.
Krylov-Levenberg-Marquardt Algorithm for Structured Tucker Tensor Decompositions
IEEE Journal of Selected Topics in Signal Processing, 2021Structured Tucker tensor decomposition models complete or incomplete multiway data sets (tensors), where the core tensor and the factor matrices can obey different constraints. The model includes block-term decomposition or canonical polyadic decomposition as special cases.
Petr Tichavský +2 more
openaire +2 more sources
Estimating fuzzy membership functions parameters by the Levenberg-Marquardt algorithm
2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542), 2005In previous papers from the authors fuzzy model identification methods were discussed. The bacterial algorithm for extracting fuzzy rule base from a training set was presented. The Levenberg-Marquardt algorithm was also proposed for determining membership functions in fuzzy systems.
Botzheim, J. +3 more
openaire +3 more sources
A Levenberg–Marquardt algorithm with correction for singular system of nonlinear equations
Applied Mathematics and Computation, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jinyan Fan, Jinlong Zeng
openaire +2 more sources
Parallel Levenberg-Marquardt Algorithm Without Error Backpropagation
2017This paper presents a new parallel architecture of the Leven-berg-Marquardt (LM) algorithm for training fully connected feedforward neural networks, which will also work for MLP but some cells will stay empty. This approach is based on a very interesting idea of learning neural networks without error backpropagation. The presented architecture is based
Jaroslaw Bilski, Bogdan M. Wilamowski
openaire +1 more source
An online actor-critic learning approach with Levenberg-Marquardt algorithm
The 2011 International Joint Conference on Neural Networks, 2011This paper focuses on the efficiency improvement of online actor-critic design base on the Levenberg-Marquardt (LM) algorithm rather than traditional chain rule. Over the decades, several generations of adaptive/approximate dynamic programming (ADP) structures have been proposed in the community and demonstrated many successfully applications.
Zhen Ni +3 more
openaire +2 more sources
A performance study of Levenberg — Marquardt (LM) Algorithm in Echo Estimation
2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2018Estimation of backscattered echoes have found many applications, in sonars, radars, medical applications etc. For the estimation purpose, backscattered echoes are modeled as a nonlinear function, using Gaussian model. Since we are using nonlinear function there are many estimation techniques like Maximum Likelihood Estimation (MLE), Bayes Estimation ...
S. Sivaprasad, Prasanth M. Warrier
openaire +1 more source
Solution to the rational function model based on the Levenberg-Marquardt algorithm
2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, 2012Conventional method of solving Rational Function Coefficients is based on the Least Squares Estimation. When there are large number of coefficients or the control points are not well-distributed, the normal equation will become ill-conditioned and the Least Squares Estimation cannot get reliable solution.
Qing Zhou, Weili Jiao, Tengfei Long
openaire +1 more source
Modifying Weights Layer-By-Layer with Levenberg-Marquardt Backpropagation Algorithm
Intelligent Automation & Soft Computing, 2001Abstract Training feedforward networks, layer–by–layer, with the Levenberg–Marquardt back–propagation algorithm is presented in this paper. The Levenberg–Marquardt backpropagation technique has been noted as an efficient method for training feedforward neural networks in terms of training accuracy, convergence properties and overall training time.
Shouling He +2 more
openaire +1 more source
Neighborhood based Levenberg-Marquardt algorithm for neural network training
IEEE Transactions on Neural Networks, 2002Although the Levenberg-Marquardt (LM) algorithm has been extensively applied as a neural-network training method, it suffers from being very expensive, both in memory and number of operations required, when the network to be trained has a significant number of adaptive weights.
Gabriel Lera, Miguel Pinzolas
exaly +3 more sources
Optimisation Using Levenberg-Marquardt Algorithm of Neural Networks for Iris
2014This paper explores the optimisation technique of Damped Least Square Method also known as the Levenberg-Marquardt (LM) Algorithm for Iris recognition. The motive behind it is to show that even though there are many algorithms available which act as an alternative to the LM algorithm such as the simple gradient decent and other conjugate gradient ...
Asim Sayed +2 more
openaire +1 more source