DocumentCode :
32759
Title :
Robust Synchronization of Multiple Memristive Neural Networks With Uncertain Parameters via Nonlinear Coupling
Author :
Shaofu Yang ; Zhenyuan Guo ; Jun Wang
Author_Institution :
Dept. of Mech. & Autom. Eng., Chinese Univ. of Hong Kong, Hong Kong, China
Volume :
45
Issue :
7
fYear :
2015
fDate :
Jul-15
Firstpage :
1077
Lastpage :
1086
Abstract :
This paper is concerned with the global robust synchronization of multiple memristive neural networks (MMNNs) with nonidentical uncertain parameters. A coupling scheme is introduced, in a general topological structure described by a direct or undirect graph, with a linear diffusive term and a discontinuous sign term. First, a set of sufficient conditions are derived based on the Lyapunov stability theory for ascertaining global robust synchronization of coupled MMNNs. Second, a pinning adaptive coupling method is proposed to ensure global synchronization without knowing the bound of parameter uncertainties. Two illustrative examples are discussed to substantiate the theoretical results.
Keywords :
Lyapunov methods; directed graphs; neural nets; stability; synchronisation; Lyapunov stability theory; MMNNs; direct graph; discontinuous sign term; general topological structure; global robust synchronization; linear diffusive term; multiple memristive neural networks; nonidentical uncertain parameters; nonlinear coupling scheme; pinning adaptive coupling method; sufficient conditions; uncertain parameters; undirect graph; Biological neural networks; Couplings; Manganese; Multi-layer neural network; Robustness; Synchronization; Uncertain systems; Global robust synchronization; memristive neural networks (MNNs); nonlinear coupling; pinning adaptive coupling;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics: Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
2168-2216
Type :
jour
DOI :
10.1109/TSMC.2014.2388199
Filename :
7018050
Link To Document :
بازگشت