Event‐triggered dissipative synchronization for Markovian jump neural networks with general transition probabilities

Abstract

Summary In this paper, dissipative synchronization problem for the Markovian jump neural networks with time‐varying delay and general transition probabilities is investigated. An event‐triggered communication scheme is introduced to trigger the transmission only when the variation of the sampled vector exceeds a prescribed threshold condition. The transition probabilities of the Markovian jump delayed neural networks are allowed to be known, or uncertain, or unknown. By employing delay system approach, a new model of synchronization error system is proposed. Applying the Lyapunov‐Krasovskii functional and integral inequality combining with reciprocal convex technique, a delay‐dependent criterion is developed to guarantee the stochastic stability of the errors system and achieve strict (Q,S,R)−α dissipativity. The event‐triggered parameters can be derived by solving a set of linear matrix inequalities. A numerical example is presented to illustrate the effectiveness of the proposed design method.

Strictly dissipative stabilization of multiple‐memory Markov jump systems with general transition rates: A novel event‐triggered control strategy

“…Therefore, a core problem is to achieve a controller that less transmission burden can be carried. Event‐triggered communication scheme (ETCS), in which the transmission of sampled signals between the controller and the plant occurs only when a predefined condition is satisfied rather than periodically as the case of traditional setups, has thus been introduced since it efficiently utilizes the network bandwidth, and a wealth of literature has appeared for this topic . To mention a few, in the work of Cheng et al, the problem of event‐triggered control (ETC) for a class of fuzzy MJSs with general switching policies has been investigated.…”

“…Event-triggered communication scheme (ETCS), in which the transmission of sampled signals between the controller and the plant occurs only when a predefined condition is satisfied rather than periodically as the case of traditional setups, has thus been introduced since it efficiently utilizes the network bandwidth, and a wealth of literature has appeared for this topic. [28][29][30][31][32][33][34][35][36] To mention a few, in the work of Cheng et al, 14 the problem of event-triggered control (ETC) for a class of fuzzy MJSs with general switching policies has been investigated. By designing a controller via an ETCS, Shen et al 37 have studied the stabilization problem of T-S fuzzy MJSs over a finite time interval.…”

Event‐triggered finite‐time reliable control for nonhomogeneous Markovian jump systems

“…It is more reasonable to use the event-triggered scheme to improve the transmission efficiency. Some efforts about event-triggered control and filtering problems have been done (see [29][30][31][32][33][34] and references therein). Furthermore, it is quite universal in practical systems that actuator faults and time delays may degrade system performance and even lead to fatal accidents.…”

Exponential Stability of Antiperiodic Solution for BAM Neural Networks with Time-Varying Delays

“…It is known that neural networks have been applied in numerous fields, such as pattern recognition, classification, associative memory, optimization, signal and image processing, parallel computation, and nonlinear optimization problems. Up to now, there are many works focusing on the dynamical nature of various kinds of neural networks, such as stability, periodic solution, almost periodic solution, bifurcation, and chaos (see [1][2][3][4][5][6][7][8][9][10][11]). However, since significant time delays are ubiquitous, it is necessary to introduce delays into communication channels which leads to delayed neural networks models.…”