Paper Info
Title
Improved Global Exponential Stability Criterion for BAM Neural Networks with Time-Varying Delays
Abstract
In this paper, the global exponential stability analysis is investigated for a class of bidirectional associative memory (BAM) neural networks with time-varying delays. By using Lyapunov functional method, and by reserving the useful terms when estimating the upper bound of the derivative of Lyapunov functional, the less conservative exponential stability criterion is derived in terms of linear matrix inequality (LMI). Numerical example is presented to show the effectiveness and the less conservativeness of the proposed method.
Year
DOI
Venue
2008
10.1007/978-3-540-87732-5_15
ISNN (1)
Keywords
Field
DocType
bidirectional associative memory,useful term,lyapunov functional method,neural network,time-varying delays,global exponential stability analysis,bam neural networks,linear matrix inequality,numerical example,improved global exponential stability,conservative exponential stability criterion,time-varying delay,lyapunov function,exponential stability,upper bound
Bidirectional associative memory,Control theory,Upper and lower bounds,Exponential stability,Artificial neural network,Lyapunov functional,Mathematics,Linear matrix inequality
Conference
Volume
ISSN
Citations 
5263
0302-9743
0
PageRank 
References 
Authors
0.34
6
2
Name
Order
Citations
PageRank
Yonggang Chen126720.44
Tiheng Qin200.68