Adjusting Weights and Architecture of Neural Networks through PSO with Time-Varying Parameters and Early Stopping

This paper presents results of an approach to optimize architecture and weights of MLP Neural Networks, which is based on particle swarm optimization with time-varying parameters and early stopping criteria. This approach was shown to achieve a good generalization control, as well as similar or bett...

Full description

Saved in:
Bibliographic Details
Main Authors: Teixeira, L.A., Oliveira, F.T.G., Oliveira, A.L.I., Filho, C.
Format: Conference Proceeding
Language:English
Subjects:
Architecture Selection
Artificial neural network
Automatic generation control
Backpropagation algorithms
Computational efficiency
Computer architecture
Computer networks
Neural networks
Particle swarm optimization
Space exploration
Time varying systems
Topology
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents results of an approach to optimize architecture and weights of MLP Neural Networks, which is based on particle swarm optimization with time-varying parameters and early stopping criteria. This approach was shown to achieve a good generalization control, as well as similar or better results than other techniques, but with a lower computational cost, with the ability to generate small networks and with the advantage of the automated architecture selection, which simplify the training process.
ISSN:1522-4899
2375-0235
DOI:10.1109/SBRN.2008.18