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Event‐triggered dissipative synchronization for Markovian jump neural networks with general transition probabilities
Author(s) -
Liu Yajuan,
Park Ju H.,
Guo BaoZhu,
Fang Fang,
Zhou Funa
Publication year - 2018
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.4110
Subject(s) - synchronization (alternating current) , dissipative system , control theory (sociology) , artificial neural network , jump , mathematics , convex combination , linear matrix inequality , computer science , stability (learning theory) , set (abstract data type) , markov process , event (particle physics) , regular polygon , convex optimization , mathematical optimization , topology (electrical circuits) , control (management) , physics , artificial intelligence , geometry , quantum mechanics , combinatorics , machine learning , programming language , statistics
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.