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Event‐triggered H∞$$ {H}_{\infty } $$ performance state estimation for neural networks with time‐varying delay.
- Source :
-
Mathematical Methods in the Applied Sciences . Mar2024, p1. 11p. 2 Illustrations. - Publication Year :
- 2024
-
Abstract
- The issue of event‐triggered H∞$$ {H}_{\infty } $$ performance state estimation for neural networks with time‐varying delays is addressed in this paper. An innovative event‐triggered approach is presented, designed to strike a harmonious equilibrium between the state estimator's performance and the communication bandwidth of the network. The proposed approach captures the relationship between the time‐varying delay and system states by employing a delay derivative‐dependent integral inequality with matrices that account for delay derivatives. This novel formulation ensures the asymptotic stability of the estimation error system, thereby fulfilling the H∞$$ {H}_{\infty } $$ performance requirement. The desired event‐triggered estimators are then obtained through the use of linear matrix inequalities. The effectiveness of this approach is demonstrated through a numerical example. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01704214
- Database :
- Academic Search Index
- Journal :
- Mathematical Methods in the Applied Sciences
- Publication Type :
- Academic Journal
- Accession number :
- 175748493
- Full Text :
- https://doi.org/10.1002/mma.9979