https://doi.org/10.1140/epjs/s11734-024-01240-0
Regular Article
Design of an event-triggered extended dissipative state estimator for neural networks with multiple time-varying delays
1
Department of Mechanical Engineering, Indian Institue of Science, 560012, Bangalore, Karnataka, India
2
Department of Mathematics, The Gandhigram Rural Institute (Deemed to be University), 624302, Gandhigram, Tamil Nadu, India
3
Institute of Mathematics and Mathematical Modeling, 050010, Almaty, Kazakhstan
4
Department of Mathematics, Nazarbayev University, 010000, Astana, Kazakhstan
Received:
18
April
2024
Accepted:
2
July
2024
Published online:
19
July
2024
This paper examines the issue of designing an extended dissipative state estimator for a class of neural networks with multiple time-varying delays. The novelty of this problem lies in assuming distinct time-varying delays for each node, demonstrating its generalizability and complexity. An event-triggered state estimator with a known output measurement is proposed to facilitate these targeted network responses by saving limited communication resources. Consequently, sufficient conditions for an extended dissipative estimator have been achieved by constructing an augmented Lyapunov–Krasovskii functional (LKF) and finding its derivative. A generalized free-weighting matrix inequality (GFWMI) is utilized to achieve a tighter upper bound of the derivative, leading to a less conservative result in linear matrix inequalities (LMIs). Ultimately, a numerical example is shown to verify the advantages and efficacy of the main findings.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
