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Event-triggered dissipative synchronization for Markovian jump neural networks with general transition probabilities. (English) Zbl 1397.93199

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 \((Q,S,R)-\alpha\) 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.

MSC:

93E03 Stochastic systems in control theory (general)
93E15 Stochastic stability in control theory
93C65 Discrete event control/observation systems
60J75 Jump processes (MSC2010)
68T05 Learning and adaptive systems in artificial intelligence
92B20 Neural networks for/in biological studies, artificial life and related topics
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