https://doi.org/10.1140/epjs/s11734-025-02004-0
Regular Article
Design of an event-triggered extended dissipative state estimator for Multi-link memristive neural networks with mixed delays
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, 632014, Vellore, Tamilnadu, India
a
This email address is being protected from spambots. You need JavaScript enabled to view it.
Received:
14
April
2025
Accepted:
1
October
2025
Published online:
12
October
2025
Abstract
This work addresses the design of an extended dissipative state estimator for multi-link memristive neural networks (MLMNNs) with mixed time delays. To optimize communication efficiency and reduce resource consumption, an event-triggered state estimation strategy is proposed, leveraging known output measurements to ensure accurate and efficient network responses. By proposing a Lyapunov–Krasovskii functional (LKF) and deriving its properties, sufficient conditions for the proposed extended dissipative state estimator are obtained through Linear matrix inequalities (LMIs). The methodology employs a relaxed mixed convex combination lemma (RMCCL), where the reciprocally convex combination lemma (RCCL) initiate delay-squared components within the differentiation of the LKF, while the quadratic convex combination lemma (QCCL) alleviates the strict negativity requirement for second-degree polynomial functions. To validate the theoretical framework, an example with numerical validation is presented, illustrating the effectiveness and superiority of the proposed approach.
Copyright comment 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.
© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2025
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.
