A new augmented Lyapunov–Krasovskii functional approach to exponential passivity for neural networks with time-varying delays

In this paper, the problem of exponential passivity analysis for uncertain neural networks with time-varying delays is considered. By constructing new augmented Lyapunov–Krasovskii’s functionals and some novel analysis techniques, improved delay-dependent criteria for checking the exponential passiv...

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Bibliographic Details
Published in:Applied mathematics and computation Vol. 217; no. 24; pp. 10231 - 10238
Main Authors: Kwon, O.M., Park, Ju H., Lee, S.M., Cha, E.J.
Format: Journal Article
Language:English
Published: Amsterdam Elsevier Inc 15.08.2011
Elsevier
Subjects:
Algorithms
Calculus of variations and optimal control
Construction
Criteria
Delay
Exact sciences and technology
Exponential passivity
Functional analysis
LMI
Lyapunov method
Mathematical analysis
Mathematical models
Mathematics
Neural networks
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in mathematical programming, optimization and calculus of variations
Numerical methods in optimization and calculus of variations
Optimization
Passivity
Sciences and techniques of general use
Time-varying delays
Exponential passivity
Lyapunov method
LMI
Time-varying delays
Neural networks
Optimization method
Exponential analysis
Functional analysis
Neural network
Algorithm
Lyapunov functional
Numerical analysis
Applied mathematics
Variational calculus
Mathematical programming
ISSN:0096-3003, 1873-5649
Online Access:Get full text
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Summary:In this paper, the problem of exponential passivity analysis for uncertain neural networks with time-varying delays is considered. By constructing new augmented Lyapunov–Krasovskii’s functionals and some novel analysis techniques, improved delay-dependent criteria for checking the exponential passivity of the neural networks are established. The proposed criteria are represented in terms of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms. A numerical example is included to show the superiority of our results.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2011.05.021