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Event-Triggered Generalized Dissipativity Filtering for Neural Networks With Time-Varying Delays.
- Source :
-
IEEE Transactions on Neural Networks & Learning Systems . Jan2016, Vol. 27 Issue 1, p77-88. 12p. - Publication Year :
- 2016
-
Abstract
- This paper is concerned with event-triggered generalized dissipativity filtering for a neural network (NN) with a time-varying delay. The signal transmission from the NN to its filter is completed through a communication channel. It is assumed that the network measurement of the NN is sampled periodically. An event-triggered communication scheme is introduced to design a suitable filter such that precious communication resources can be saved significantly while certain filtering performance can be ensured. On the one hand, the event-triggered communication scheme is devised to select only those sampled signals violating a certain threshold to be transmitted, which directly leads to saving of precious communication resources. On the other hand, the filtering error system is modeled as a time-delay system closely dependent on the parameters of the event-triggered scheme. Based on this model, a suitable filter is designed such that certain filtering performance can be ensured, provided that a set of linear matrix inequalities are satisfied. Furthermore, since a generalized dissipativity performance index is introduced, several kinds of event-triggered filtering issues, such as $H_\infty $ filtering, passive filtering, mixed H_\infty $ and passive filtering, (Q,S,R)$ -dissipative filtering, and – \mathcal L_{\infty } filtering, are solved in a unified framework. Finally, two examples are given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 27
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 111967202
- Full Text :
- https://doi.org/10.1109/TNNLS.2015.2411734