Keywords
H∞ state estimation
Memristive neural networks
Resource-efficient estimation
Variance-constrained estimation
Dynamic event-triggered mechanism
How to Cite
Abstract
This paper discusses the dynamic event-triggered H∞ state estimation issue for memristive neural networks with time-delay under variance constraints. The dynamic event-triggered mechanism is incorporated into the sensor-to-estimator to reduce resource consumption in the communication channel. The objective is to design the time-varying state estimator such that, in the presence of the dynamic event-triggered mechanism and time-delay, new sufficient criteria are derived to ensure the desired H∞ performance and the boundedness of estimation error variance. Furthermore, a novel non-augmented H∞ state estimation algorithm is proposed under variance constraint by using the stochastic analysis techniques. Finally, a simulation example is used to illustrate the effectiveness of the proposed H∞ state estimation algorithm.
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