TY  - JOUR
T1  - Fast Training of Multilayer Perceptrons with Least Mean Fourth (LMF) Algorithm
AU - , Sabeur Abid AU - , Farhat Fnaiech 
JO  - International Journal of Soft Computing
VL  - 3
IS  - 5
SP  - 359
EP  - 367
PY  - 2008
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2008.359.367
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2008.359.367
KW  - least fourth error
KW  -least square error
KW  -Standard Back Propagation (SBP) algorithm
KW  -Multilayer Perceptron (MLP) Fast training algorithms
AB  - In this study, we propose a new fast training algorithm for multilayer perceptron (MLP). This new algorithm is based on the optimisation of Least Fourth (LF) criterion producing a modified form of the Standard Back-Propagation (SBP) algorithm. In this criterion, the Least Fourth error signal is appropriately weighed by the learning coefficient of the steepest descent method. To determine the updating rules for the hidden layers, a similar back propagation method used in the SBP algorithm is developed. This permits the application of the learning procedure to all the neural network layers. Several experiments was carried out indicate significant reduction in the total iteration number, in the convergence time and in the generalization capacities when compared to those of the SBP algorithm.
ER  -