Stability analysis of bidirectional associative memory networks with time delays

By using the method of Liapunov functional, a model for bidirectional associative memory networks with time delays is studied. The asymptotic stability is global in the state space of the neuronal activations and is also independent of the delays. Our results can be applied to a variety of situation...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2003-11, Vol.14 (6), p.1560-1565
Main Authors: Chunhua Feng, Plamondon, R.
Format: Article
Language:English
Subjects:
Activation
Artificial neural networks
Associative memory
Asymptotic properties
Asymptotic stability
Bidirectional
Biological system modeling
Circuits
Convergence
Delay
Delay effects
Magnesium compounds
Networks
Neural networks
Stability analysis
State-space methods
Time delay
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:By using the method of Liapunov functional, a model for bidirectional associative memory networks with time delays is studied. The asymptotic stability is global in the state space of the neuronal activations and is also independent of the delays. Our results can be applied to a variety of situations that arise both in the field of biological and artificial neural networks.
ISSN:1045-9227
2162-237X
1941-0093
2162-2388
DOI:10.1109/TNN.2003.820829