LMI-based robust exponential stability criterion of impulsive integro-differential equations with uncertain parameters via contraction mapping theory

By formulating a new contraction mapping on a product space, the authors originally employed Banach fixed point theorem to derive the LMI-based robust exponential stability criterion for impulsive BAM neural networks with distributed delays and uncertain parameters. Numerical example illuminates tha...

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Bibliographic Details
Published inAdvances in difference equations Vol. 2017; no. 1; pp. 1 - 16
Main Authors Rao, Ruofeng, Zhong, Shouming, Pu, Zhilin
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 18.01.2017
Springer Nature B.V
Subjects
Analysis
Criteria
Difference and Functional Equations
Functional Analysis
Mapping
Mathematical analysis
Mathematical models
Mathematics
Mathematics and Statistics
Ordinary Differential Equations
Parameter robustness
Parameter uncertainty
Partial Differential Equations
Proceedings of the International Conference on Analysis and Applications
Stability
Stability criteria
parameters uncertainty
contraction mapping theory
impulsive BAM neural networks
robust exponential stability
integro-differential equations
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Summary:By formulating a new contraction mapping on a product space, the authors originally employed Banach fixed point theorem to derive the LMI-based robust exponential stability criterion for impulsive BAM neural networks with distributed delays and uncertain parameters. Numerical example illuminates that the new criterion is not worse than the existing results derived by Lyapunov functional method. Hence both the methods and the results of this paper are really novel to a certain extent.
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ISSN:1687-1847
1687-1839
1687-1847
DOI:10.1186/s13662-016-1059-0