Event‐triggered synchronization for stochastic delayed neural networks: Passivity and passification case

Abstract

This article investigates the event-triggered synchronization problem of stochastic neural networks under passivity and passification cases. For saving communication resources, an event-triggered approach is engaged in the design of synchronization for the delayed stochastic neural networks. To decrease network trouble, an event-triggered scheme is suggested between the sampler and communication network. A nonfragile event-triggered controller is intended to guarantee the finite-time stability of the subsequent closed-loop system. By applying the Lyapunov-Krasovkii functional (LKF) and the novel integral inequalities, a stability criteria for an interval-time varying delay error system ensure the designed controller can fulfill the necessities of passivity and passification performance. The desired control gain and event-triggered parameters are then found based on the linear matrix inequalities (LMIs). Finally, illustrative examples are given to show the benefits and validity of the desired control law.

Stabilization of Stochastic Dynamical Systems of a Random Structure with Markov Switches and Poisson Perturbations

“…This assumption of quasi-linearity allows, with additional conditions on the value of the nonlinear part, the discovery of sufficient stability conditions of the x(t), t ≥ 0, by constructing suboptimal control u(t), t ≥ 0. Similar results were obtained also in the work [16], where authors described an algorithm of stabilization by construction of the non-fragile event-triggered controller for Ito stochastic differential equations with varying delay. The authors chose a specific class of admissible controls, which makes it possible to solve the optimization problem for finding a suboptimal control.…”

“…It should be noted that a large number of works are devoted to the issues of stability of systems with jumping Markov processes. For example, the works [15,16] consider sufficient conditions for the stability of Ito stochastic differential equations with Markov switching and the presence of variable delay. In the work [15], this theory has gained logical use for modeling neural networks with a decentralized event-triggered mechanism and finding sufficient conditions for stabilizing the process that describe dynamic of the neural network.…”

A Parameter‐Uniform Numerical Scheme for Solving Singularly Perturbed Parabolic Reaction‐Diffusion Problems with Delay in the Spatial Variable

“…Diferent model problems in science and engineering are described using delay diferential equations. For instance, in the modeling of biological mathematics [2], problems in control theory [3], in modeling fnite-time event-triggered synchronization for stochastic neural networks along with the passivity and passifcation conditions [4], human pupil light refex [5], HIV infections [6], in modeling the upper bound for bidirectional associative memory neural networks under global asymptotic robust stability condition [7], COVID-19 pandemic [8], in modeling sampled-data state estimation for delayed Markovian jamp neural networks based on passive theory [9] and others.…”

Synchronization of stochastic memristive neural networks via event‐triggered impulsive control

“…Recently, event-triggered control (ETC) has been shown to have an excellent ability to reduce the utilization of communication resources in many papers. [18][19][20][21] Under the well-designed ETM, data will be transmitted through the communication networks only when the certain state-dependent triggering conditions are met or violated. By combining impulsive control with ETC strategy, that is, ETIC, impulsive instants can be adaptively adjusted based on system states.…”

“…With widespread applications of networked communication technology in control systems, it is crucial to lighten the communication burden while ensuring the ideal control effect. Recently, event‐triggered control (ETC) has been shown to have an excellent ability to reduce the utilization of communication resources in many papers 18‐21 . Under the well‐designed ETM, data will be transmitted through the communication networks only when the certain state‐dependent triggering conditions are met or violated.…”