Title :
Pinning Distributed Synchronization of Stochastic Dynamical Networks: A Mixed Optimization Approach
Author :
Yang Tang ; Huijun Gao ; Jianquan Lu ; Kurths, J.
Author_Institution :
Potsdam Inst. for Climate Impact Res., Potsdam, Germany
Abstract :
This paper is concerned with the problem of pinning synchronization of nonlinear dynamical networks with multiple stochastic disturbances. Two kinds of pinning schemes are considered: 1) pinned nodes are fixed along the time evolution and 2) pinned nodes are switched from time to time according to a set of Bernoulli stochastic variables. Using Lyapunov function methods and stochastic analysis techniques, several easily verifiable criteria are derived for the problem of pinning distributed synchronization. For the case of fixed pinned nodes, a novel mixed optimization method is developed to select the pinned nodes and find feasible solutions, which is composed of a traditional convex optimization method and a constraint optimization evolutionary algorithm. For the case of switching pinning scheme, upper bounds of the convergence rate and the mean control gain are obtained theoretically. Simulation examples are provided to show the advantages of our proposed optimization method over previous ones and verify the effectiveness of the obtained results.
Keywords :
Lyapunov methods; convex programming; evolutionary computation; multi-robot systems; nonlinear dynamical systems; stochastic systems; synchronisation; Bernoulli stochastic variables; Lyapunov function methods; constraint optimization evolutionary algorithm; convergence rate upper bound; convex optimization method; mean control gain; mixed optimization approach; multiagent systems; nonlinear dynamical networks; pinned nodes; pinning distributed synchronization; stochastic analysis techniques; stochastic disturbances; stochastic dynamical networks; switching pinning scheme; Complex networks; Nickel; Optimization methods; Stochastic processes; Switches; Synchronization; Complex networks; evolutionary algorithms (EAs); multiagent systems; neural networks; stochastic disturbances; synchronization; synchronization.;
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
DOI :
10.1109/TNNLS.2013.2295966