Published November 2004 | Version v1
Journal article

Globally exponentially robust stability and periodicity of delayed neural networks

Description

In this paper, a new concept of robust periodicity is introduced, and problem of robust stability and robust periodicity is discussed for delayed neural networks. Several sufficient conditions are derived for globally exponentially robust stability and robust periodicity of delayed neural networks based Lyapunov method. These results improve and extend those given in the earlier references

Additional details

Identifiers

DOI
10.1016/j.chaos.2004.03.019;
PII
S0960077904001791;

Publishing Information

Journal Title
Chaos, Solitons and Fractals
Journal Volume
22
Journal Issue
4
Journal Page Range
p. 957-963
ISSN
0960-0779

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
35056126
Subject category
S71: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS;
Descriptors DEI
LYAPUNOV METHOD; NEURAL NETWORKS; PERIODICITY; STABILITY
Descriptors DEC
CALCULATION METHODS; VARIATIONS

Optional Information

Copyright
Copyright (c) 2004 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.