Paper Info
Title
Event-Based Dissipative Analysis for Discrete Time-Delay Singular Jump Neural Networks.
Abstract
This paper investigates the event-triggered dissipative filtering issue for discrete-time singular neural networks with time-varying delays and Markovian jump parameters. Via event-triggered communication technique, a singular jump neural network (SJNN) model of network-induced delays is first given, and sufficient criteria are then provided to guarantee that the resulting augmented SJNN is stochastically admissible and strictly stochastically dissipative (SASSD) with respect to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(\mathcal {X}_{\iota },\mathcal {Y}_{\iota },\mathcal {Z}_{\iota },\delta)$ </tex-math></inline-formula> by using slack matrix scheme. Furthermore, employing filter equivalent technique, codesigned filter gains, and event-triggered matrices are derived to make sure that the augmented SJNN model is SASSD with respect to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(\mathcal {X}_{\iota },\mathcal {Y}_{\iota },\mathcal {Z}_{\iota },\delta)$ </tex-math></inline-formula> . An example is also given to illustrate the effectiveness of the proposed method.
Year
DOI
Venue
2020
10.1109/TNNLS.2019.2919585
IEEE transactions on neural networks and learning systems
Keywords
DocType
Volume
Dissipativity,event-based communication technique,Markovian jump parameters,singular neural networks,time-varying delays
Journal
31
Issue
ISSN
Citations 
4
2162-237X
4
PageRank 
References 
Authors
0.37
20
4
Name
Order
Citations
PageRank
Yingqi Zhang114611.82
Peng Shi215816704.36
ramesh k agarwal3979.86
Yan Shi428527.64