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Stochastic distribution synchronization and pinning control for complex heterogeneous dynamical networks
Author(s) -
Wang Guoqiang,
Ji Jinchen,
Zhou Jin
Publication year - 2020
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.2044
Subject(s) - synchronization (alternating current) , ergodic theory , dynamical systems theory , complex network , stochastic process , stochastic neural network , statistical physics , stochastic modelling , mathematics , continuous time stochastic process , topology (electrical circuits) , computer science , control theory (sociology) , stochastic optimization , artificial neural network , mathematical optimization , control (management) , mathematical analysis , physics , artificial intelligence , recurrent neural network , combinatorics , statistics , quantum mechanics
This paper investigates the stochastic synchronization and pinning control in the sense of probability distribution for a general model of complex heterogeneous dynamical networks subjected to stochastic disturbances. Some generic stochastic synchronization criteria are established for both cases of undirected and directed topology by using the ergodic theory on stochastic dynamical systems. Compared with most existing studies on the stochastic synchronization in the sense of mean square, it is demonstrated that the concept of stochastic distribution synchronization can well characterize the realistic structure and essential nature of complex practical stochastic systems. Subsequently, two representative examples of complex heterogeneous dynamical networks, namely coupled stochastic Duffing oscillators and coupled FitzHugh‐Nagumo neuron oscillators, are given to illustrate and numerically verify the theoretical results.