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Almost surely asymptotic synchronization for stochastic neural networks of neutral type with Markovian jumping parameters
Author(s) -
Wu Tao,
Xiong Lianglin,
Cao Jinde,
Xie Xueqin
Publication year - 2019
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.3047
Subject(s) - synchronization (alternating current) , control theory (sociology) , mathematics , markov process , type (biology) , invariance principle , matlab , jumping , artificial neural network , computer science , control (management) , topology (electrical circuits) , artificial intelligence , physiology , ecology , linguistics , statistics , philosophy , combinatorics , biology , operating system
Summary This paper studies the problem of the almost surely asymptotic synchronization for a class of stochastic neural networks of neutral type with both Markovian jumping parameters and mixed time delays. Based on the stochastic analysis theory, LaSalle‐type invariance principle, and delayed state‐feedback control technique, some novel delay‐dependent sufficient criteria to guarantee the almost surely asymptotic synchronization are given. These criteria are expressed as the linear matrix inequalities, which can be easily checked by MATLAB LMI Control Toolbox. Finally, four numerical examples and their simulations are provided to illustrate the effectiveness of the proposed method.