Title :
Training feedforward networks with the Marquardt algorithm
Author :
Hagan, Martin T. ; Menhaj, Mohammad B.
fDate :
11/1/1994 12:00:00 AM
Abstract :
The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks. The algorithm is tested on several function approximation problems, and is compared with a conjugate gradient algorithm and a variable learning rate algorithm. It is found that the Marquardt algorithm is much more efficient than either of the other techniques when the network contains no more than a few hundred weights
Keywords :
backpropagation; feedforward neural nets; function approximation; least squares approximations; Marquardt algorithm; backpropagation; feedforward network training; feedforward neural networks; function approximation; learning; nonlinear least squares; Acceleration; Approximation algorithms; Backpropagation algorithms; Convergence; Feedforward neural networks; Function approximation; Least squares approximation; Least squares methods; Neural networks; Testing;
Journal_Title :
Neural Networks, IEEE Transactions on
