This article examines the issue of event-triggered $L_{2}-L_\infty $ filtering for network-based neutral systems via Takagi-Sugeno (T-S) fuzzy approach. A dynamic discrete event-triggered scheme (ETS) is introduced to save the limited communication resource. Based on the T-S fuzzy model, the consider neutral type system with networked induced delays are represented as a class of T-S fuzzy system. In addition, by considering a suitable Lyapunov-Krasovskii functional (LKF) and by using the Wirtinger inequality technique, the stability conditions with respect to linear matrix inequalities (LMIs) are presented to guarantee the considered filtering systems are asymptotically stable with $L_{2}-L_{\infty }$ performance index $\gamma $ . To the end, numerical examples are given to illustrate the effectiveness of the proposed result.

We discuss stability analysis for uncertain stochastic neural networks (SNNs) with time delay in this letter. By constructing a suitable Lyapunov-Krasovskii functional (LKF) and utilizing Wirtinger inequalities for estimating the integral inequalities, the delay-dependent stochastic stability conditions are derived in terms of linear matrix inequalities (LMIs). We discuss the parameter uncertainties in terms of norm-bounded conditions in the given interval with constant delay. The derived conditions ensure that the global, asymptotic stability of the states for the proposed SNNs. We verify the effectiveness and applicability of the proposed criteria with numerical examples.

This paper presents an extended dissipativity-based sampled-data controller design for Fuzzy distributed parameter systems (DPSs). Then the proposed DPSs solved by integral inequality techniques to construct a Lyapunov-Krasovskii functional (LKF) that demonstrates the stability and stabilization of the dissipativity performance of DPSs. The fuzzy-based sampled-data control (FSDC) scheme is obtained by solving linear matrix inequalities (LMIs), ensuring that the closed-loop system is extended dissipativity. The FSDC can be adjusted to achieve various performance goals, such as $ L_2-L_{\infty} $, $ H_{\infty} $, passivity, and $ (Q, S, R)-\gamma $-dissipative performance for DPSs. The proposed method is verified through simulations using the MATLAB LMI control toolbox.