Title
l2-l∞ state estimation for discrete-time switched neural networks with time-varying delay.
Abstract
This paper is concerned with the l2−l∞ state estimation problem for discrete-time switched neural networks with time-varying delay. The main objective is to design a mode-dependent state estimator such that the error dynamics is exponentially stable with a weighted l2−l∞ performance level. By incorporating the novel l2−l∞ performance analysis approach, the augmented piecewise Lyapunov-like functionals, the discrete Wirtinger-based inequality and the average-dwell-time switching, less conservative sufficient conditions are proposed by means of linear matrix inequalities. A numerical example is given to illustrate the effectiveness and benefits of the obtained results.
Year
DOI
Venue
2018
10.1016/j.neucom.2017.12.006
Neurocomputing
Keywords
Field
DocType
State estimation,Discrete-time,Switched neural networks,Time-varying delay,l2−l∞ performance
Applied mathematics,State estimator,Pattern recognition,Matrix (mathematics),Exponential stability,Artificial intelligence,Discrete time and continuous time,Artificial neural network,Mathematics,Piecewise
Journal
Volume
ISSN
Citations 
282
0925-2312
1
PageRank 
References 
Authors
0.34
30
5
Name
Order
Citations
PageRank
Yonggang Chen126720.44
Lili Liu220.69
Wei Qian3916.67
Yurong Liu43419162.92
Fuad E. Alsaadi51818102.89