Title :
Global Exponential Synchronization of Multiple Memristive Neural Networks With Time Delay via Nonlinear Coupling
Author :
Zhenyuan Guo ; Shaofu Yang ; Jun Wang
Author_Institution :
Coll. of Math. & Econ., Hunan Univ., Changsha, China
Abstract :
This paper presents theoretical results on the global exponential synchronization of multiple memristive neural networks with time delays. A novel coupling scheme is introduced, in a general topological structure described by a directed or undirected graph, with a linear diffusive term and discontinuous sign term. Several criteria are derived based on the Lyapunov stability theory to ascertain the global exponential stability of synchronization manifold in the coupling scheme. Simulation results for several examples are given to substantiate the effectiveness of the theoretical results.
Keywords :
Lyapunov methods; asymptotic stability; delays; directed graphs; neurocontrollers; nonlinear control systems; synchronisation; Lyapunov stability theory; directed graph; discontinuous sign term; general topological structure; global exponential stability; global exponential synchronization; linear diffusive term; multiple memristive neural networks; nonlinear coupling; time delay; undirected graph; Biological neural networks; Couplings; Indexes; Manganese; Memristors; Multi-layer neural network; Synchronization; Global exponential synchronization; memristive neural network (MNN); nonlinear coupling; synchronization manifold;
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
DOI :
10.1109/TNNLS.2014.2354432
