Event‐triggered dissipative synchronization for Markovian jump neural networks with general transition probabilities |
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Authors: | Yajuan Liu Ju H Park Bao‐Zhu Guo Fang Fang Funa Zhou |
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Affiliation: | 1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;2. Department of Electrical Engineering, Yeungnam University, Gyeongsan 38541, South Korea;3. Key Laboratory of System and Control, Academy of Mathematics and Systems Science Academia Sinica, Beijing 100190, China;4. School of Computer and Information Engineering, Henan University, Kaifeng 475004, China |
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Abstract: | 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. |
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Keywords: | dissipative synchronization event‐triggered control general transition probabilities Markovian jump neural networks |
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