Generalized dissipativity state estimation of delayed neural networks under a novel event-triggered strategy
DOI:
https://doi.org/10.54097/ygykwv31Keywords:
Delayed neural networks, Adaptive event-triggered, Generalized dissipativity state estimation, StabilityAbstract
This paper investigates the generalized dissipativity state estimation problem for delayed neural networks (DNNs) under a novel event-triggered strategy. Unlike traditional Lyapunov-Krasovskii Functionals (LKFs) which impose positive definiteness constraints on matrices, this paper constructs a novel delay-dependent LKF. The positive definiteness constraint on the LKF matrix is relaxed via a quadratic inequality, which offers more flexibility in the functional construction. Meanwhile, to guarantee satisfactory estimator performance under limited communication resources, a novel adaptive event-triggered strategy (AETS) with memory-based triggering logic is proposed. Unlike existing event-triggered (ET) strategies, the proposed AETS can dynamically adjust triggering parameters and further reduce communication frequency. Accordingly, two sufficient criteria are derived via linear matrix inequalities (LMIs) to guarantee the dissipativity of the estimation error system. Finally, numerical examples are provided to demonstrate the superiority of the proposed methods.
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