NDLI: Global asymptotic and robust stability of recurrent neural networks with time delays

Content Provider	IEEE Xplore Digital Library
Author	Jinde Cao Jun Wang
Copyright Year	2004
Abstract	In this paper, two related problems, global asymptotic stability (GAS) and global robust stability (GRS) of neural networks with time delays, are studied. First, GAS of delayed neural networks is discussed based on Lyapunov method and linear matrix inequality. New criteria are given to ascertain the GAS of delayed neural networks. In the designs and applications of neural networks, it is necessary to consider the deviation effects of bounded perturbations of network parameters. In this case, a delayed neural network must be formulated as a interval neural network model. Several sufficient conditions are derived for the existence, uniqueness, and GRS of equilibria for interval neural networks with time delays by use of a new Lyapunov function and matrix inequality. These results are less restrictive than those given in the earlier references.
Sponsorship	IEEE Circuits and Systems Society
Starting Page	417
Ending Page	426
Page Count	10
File Size	380695
File Format	PDF
ISSN	15498328
Volume Number	52
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-02-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robust stability Recurrent neural networks Delay effects Neural networks Sufficient conditions Linear matrix inequalities Asymptotic stability Stability criteria Lyapunov method Neurons time delay Global asymptotic stability (GAS) global robust stability (GRS) interval neural network linear matrix inequality (LMI) Lyapunov functional matrix inequality
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Hardware and Architecture

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