Title :
Pinning Synchronization of Directed Networks With Switching Topologies: A Multiple Lyapunov Functions Approach
Author :
Guanghui Wen ; Wenwu Yu ; Guoqiang Hu ; Jinde Cao ; Xinghuo Yu
Author_Institution :
Dept. of Math., Southeast Univ., Nanjing, China
Abstract :
This paper studies the global pinning synchronization problem for a class of complex networks with switching directed topologies. The common assumption in the existing related literature that each possible network topology contains a directed spanning tree is removed in this paper. Using tools from M-matrix theory and stability analysis of the switched nonlinear systems, a new kind of network topology-dependent multiple Lyapunov functions is proposed for analyzing the synchronization behavior of the whole network. It is theoretically shown that the global pinning synchronization in switched complex networks can be ensured if some nodes are appropriately pinned and the coupling is carefully selected. Interesting issues of how many and which nodes should be pinned for possibly realizing global synchronization are further addressed. Finally, some numerical simulations on coupled neural networks are provided to verify the theoretical results.
Keywords :
Lyapunov methods; complex networks; matrix algebra; neurocontrollers; nonlinear control systems; numerical analysis; stability; switching systems (control); synchronisation; topology; Lyapunov functions approach; M-matrix theory; coupled neural network; directed network; directed spanning tree; global pinning synchronization problem; global synchronization; network topology-dependent multiple Lyapunov function; numerical simulation; stability analysis; switched complex network; switched nonlinear system; switching directed topology; switching topology; synchronization behavior; Complex networks; Eigenvalues and eigenfunctions; Laplace equations; Switches; Synchronization; Trajectory; $M$ -matrix; $M$-matrix; Complex network; directed spanning tree; neural network; pinning synchronization; pinning synchronization.;
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
DOI :
10.1109/TNNLS.2015.2443064