Reliable ℓ2−ℓ∞ state estimation for delayed neural networks under weighted try-one-discard protocol

Abstract

In this paper, the problem of reliable ${\ell }_2$-${\ell }_{\infty }$ state estimation is addressed for discrete-time artificial neural networks with switched time-delays under weighted try-once-discard (WTOD) protocol. To mitigate data congestion and transmission burdens, the WTOD protocol is implemented in the transmission channels to optimize data communication, where the transmission priority is dynamically determined based on mission importance. A Bernoulli-distributed stochastic variable with known statistical properties is introduced to model the switching behavior between the presence and absence of time-delays and a failure matrix is constructed to characterize potential failures affecting the received measurement data. The primary objective of this paper is to develop a state estimator that effectively performs the desired estimation task by thoroughly accounting for the combined effects of switched time-delays and the WTOD protocol. Specifically, by utilizing Lyapunov theory and matrix inequality techniques, the estimator parameters are meticulously derived to ensure exponentially mean-square stability and ${\ell }_2$-${\ell }_{\infty }$ performance. Finally, the efficacy and validity of the proposed algorithm are demonstrated through an illustrative example.