Liu, Yajuan; Park, Ju H.; Guo, Bao-Zhu; Fang, Fang; Zhou, Funa Event-triggered dissipative synchronization for Markovian jump neural networks with general transition probabilities. (English) Zbl 1397.93199 Int. J. Robust Nonlinear Control 28, No. 13, 3893-3908 (2018). 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. Cited in 19 Documents 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 Keywords:dissipative synchronization; event-triggered control; general transition probabilities; Markovian jump neural networks PDFBibTeX XMLCite \textit{Y. Liu} et al., Int. J. Robust Nonlinear Control 28, No. 13, 3893--3908 (2018; Zbl 1397.93199) Full Text: DOI