Finite-Time H_∞ Estimator Design for Switched Discrete-Time Delayed Neural Networks With Event-Triggered Strategy

Event‐triggered annular finite‐time H∞$$ {H}_{\infty } $$ filtering for discrete‐time mean‐field stochastic systems

“…Remark Compared with (9) in Reference 19, the $$$ {\vartheta}_1\left({\overline{x}}_0\right) $$$ in condition (10) represents a nonnegative real function related to initial value $$$ {\overline{x}}_0 $$$. Here, $$$ {\overline{x}}_0 $$$ is nonzero due to $$$ 0<{\rho}_1\le \left\langle {\overline{x}}_0,{R}_1{\overline{x}}_0\right\rangle \le {\rho}_2 $$$ in Definition 1.…”

“…For example, the angle position of the robot arm should not exceed a given threshold in a prescribed time interval 14 . In this circumstance, finite‐time stability (FTS) was first proposed in Reference 15 and then abundantly developed in a variety of issues including FTS and finite‐time stabilization, 16 finite‐time $$$ {H}_{\infty } $$$ control 17 and guaranteed cost control 18 problems, finite‐time $$$ {H}_{\infty } $$$ filtering problems 19‐21 and so on.…”

Event-triggered Synchronization of Discrete-time CVNNs with Mixed Delays: A Switching Approach

“…In more complex scenarios, the delay is a function of time t and can be characterized by time-varying delays [17,18,19]. Meanwhile, mixed delays are relatively common when multiple different time delays exist in the model simultaneously [20,21,22,23]. Overall, these findings reveal that investigating the impact of time delay on neural networks is crucial for understanding their behavior and improving their performance.…”

Quantized fault-tolerant pinning control for finite-time H∞ synchronization of T-S fuzzy discrete Markovian-jump complex networks subject to heterogeneous delays

“…With regard to a type of drive-response networks, finite-time synchronization criteria were derived in [20] to design event-based aperiodic intermittent controller. In addition, as shown in [21], external disturbance is unavoidable in network systems and can break the system stability, such that ¥ performance analysis is developed extensively [22][23][24]. Lin et al discussed ¥ event-triggered synchronization protocol for delayed neural networks with switched and directed couplings in [22].…”

“…Lin et al discussed ¥ event-triggered synchronization protocol for delayed neural networks with switched and directed couplings in [22]. For the discrete-time switched neural networks impacted by mixed delays and packet dropouts, authors presented an estimator based on finite-time specified ¥ performance in [24]. Meanwhile, finite-time ¥ synchronization was extended to a few works concerning DCNs, such as in [25,26], but relevant methods have not been applied in analyzing TSFDMCNs due to the intricacy system structure.…”