Open AccessSynchronization for an Array of Coupled Cohen‐Grossberg Neural Networks with Time‐Varying Delay
Author(s) -
HaiTao Zhang,
Tao Li,
Shumin Fei
Publication year - 2011
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2011/831695
Subject(s) - kronecker product , synchronization (alternating current) , artificial neural network , weighting , convex combination , control theory (sociology) , product (mathematics) , computer science , matrix (chemical analysis) , linear matrix inequality , mathematics , coupling (piping) , algorithm , regular polygon , mathematical optimization , kronecker delta , convex optimization , topology (electrical circuits) , engineering , artificial intelligence , combinatorics , physics , geometry , control (management) , materials science , quantum mechanics , acoustics , composite material , mechanical engineering
This paper makes some great attempts to investigate the global exponential synchronization for arrays of coupled delayed Cohen-Grossberg neural networks with both delayed coupling and one single delayed one. By resorting to free-weighting matrix and Kronecker product techniques, two novel synchronization criteria via linear matrix inequalities (LMIs) are presented based on convex combination, in which these conditions are heavily dependent on the bounds of both the delay and its derivative. Owing to that the addressed system can include some famous neural network models as the special cases, the proposed methods can extend and improve those earlier reported ones. The efficiency and applicability of the presented conditions can be demonstrated by two numerical examples with simulations
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