Improvement of the neighborhood based Levenberg-Marquardt algorithm by local adaptation of the learning coefficient

Authors:
A. Toledo;M. Pinzolas;J. J. Ibarrola;G. Lera
Affiliations:
Dept. Tecnologia Electron., Univ. Politecnica de Cartagena, Spain;-;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
2005

Citing 0
Cited 8

A neighborhood-based enhancement of the Gauss-Newton Bayesian regularization training method

Neural Computation
Neighborhood based modified backpropagation algorithm using adaptive learning parameters for training feedforward neural networks

Neurocomputing
Design optimization of a spatial six degree-of-freedom parallel manipulator based on artificial intelligence approaches

Robotics and Computer-Integrated Manufacturing
Efficient and reliable training of neural networks

HSI'09 Proceedings of the 2nd conference on Human System Interactions
Neural network learning without backpropagation

IEEE Transactions on Neural Networks
Forward kinematics, performance analysis, and multi-objective optimization of a bio-inspired parallel manipulator

Robotics and Computer-Integrated Manufacturing
Fixed point method for autonomous on-line neural network training

Neurocomputing
Statistical methods for estimation of direct and differential kinematics of the vocal tract

Speech Communication

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this letter, an improvement of the recently developed neighborhood-based Levenberg-Marquardt (NBLM) algorithm is proposed and tested for neural network (NN) training. The algorithm is modified by allowing local adaptation of a different learning coefficient for each neighborhood. This simple add-in to the NBLM training method significantly increases the efficiency of the training episodes carried out with small neighborhood sizes, thus, allowing important savings in memory occupation and computational time while obtaining better performance than the original Levenberg-Marquardt (LM) and NBLM methods.