Paper Info
Title
Robust stability of discrete-time stochastic BAM neural networks with Markovian jumping parameters and time-varying delays
Abstract
This paper investigates the problem of robust stability for a class of uncertain discrete-time stochastic bidirectional associative memory (BAM) neural networks with Markovian jumping parameters and time-varying delays. By employing the Lyapunov functional we can get novel robust stability conditions in terms of linear matrix inequality (LMI), which can be easily solved by MATLAB LMI toolbox. Furthermore, we will introduce into some free weighting matrices in order to lead to much less conservative results. At last, one numerical example is given to illustrate the effectiveness of the proposed results.
Year
DOI
Venue
2010
10.1109/ICMLC.2010.5580654
ICMLC
Keywords
Field
DocType
robust stability,stochastic systems,time-varying delays,robust control,linear matrix inequality,stochastic,markovian jumping parameters,discrete-time stochastic bam neural networks,delays,bidirectional associative memory,discrete-time,discrete time systems,linear matrix inequalities,free weighting matrices,stability,markov processes,lyapunov methods,markovian jumping parameter,neural nets,lyapunov functional,lyapunov function,discrete time,robustness,artificial neural networks
Markov process,Bidirectional associative memory,Control theory,Computer science,Stability conditions,Robustness (computer science),Discrete time and continuous time,Robust control,Artificial neural network,Linear matrix inequality
Conference
Volume
ISBN
Citations 
5
978-1-4244-6526-2
0
PageRank 
References 
Authors
0.34
4
3
Name
Order
Citations
PageRank
Gui-Ju Shi120.77
Mifeng Ren2167.85
Jun-Ling Gao331.49