A differential adaptive learning rate method for back-propagation neural networks

Authors:
Saeid Iranmanesh
Affiliations:
Department of Computer Engineering, Azad university of Qazvin, Iran
Venue:
NN'09 Proceedings of the 10th WSEAS international conference on Neural networks
Year:
2009

Citing 1
Cited 2

Stable dynamic backpropagation learning in recurrent neural networks

IEEE Transactions on Neural Networks

Artificial neural network-based system for PET volume segmentation

Journal of Biomedical Imaging
Neural smooth function approximation and prediction with adaptive learning rate

Transactions on Computational Collective Intelligence VII

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper a high speed learning method using differential adaptive learning rate (DALRM) is proposed. Comparison of this method with other methods such as standard BP, Nguyen-Widrow weight Initialization and Optical BP shows that the network's learning speed has highly increased. Learning often takes a long time to converge and it may fall into local minimas. One way of escaping from local minima is to use a large learning rate at first and then to gradually reduce this learning rate. In this method which is used in multi-layer networks using back-propagation learning algorithm, network error is reduced in a short time using differential adaptive learning rate.